FDCR Dynamic Analysis
Rayus
Apr 9, 2020
Input
Analysis for FDCR manuscript describing the temporal dynamics of drug cue reactivity with replication. ## Read Data
#function to be able to read in an .RData file and assign it to a variable
loadRData <- function(fileName){
#loads an RData file, and returns it
load(fileName)
get(ls()[ls() != "fileName"])
}idps_neurocaps <- loadRData('../paper-dcr-temporaldynamics/data/neurocaps/idps-neurocaps-2-13-2020.RData')
idps_neurocaps$motion <- idps_neurocaps$dcr_motaveraw_r1
#remove subjects due to excessive motion
idps_neurocaps <- idps_neurocaps[idps_neurocaps$motion < 0.3 & (!idps_neurocaps$neurocaps_exclude),]
idps_neurocaps_ocr <- idps_neurocaps[idps_neurocaps$dcr_version == 'ocr',]
idps_neurocaps_mcr <- idps_neurocaps[idps_neurocaps$dcr_version == 'mcr',]
idps_tacs <- loadRData('../paper-dcr-temporaldynamics/data/tacs/idps-tacs-2-13-2020.RData')
idps_tacs$motion <- idps_tacs$dcr_tpre_motaveraw_r1
idps_tacs <- idps_tacs[idps_tacs$motion < 0.3 & (!idps_tacs$tacs_exclude),]
idps_tdcs <- loadRData('../paper-dcr-temporaldynamics/data/tdcs/idps-tdcs-2-13-2020.RData')
idps_tdcs$motion <- idps_tdcs$dcr_tpre_motaveraw_r1
idps_tdcs <- idps_tdcs[idps_tdcs$motion < 0.3 & (!idps_tdcs$tdcs_exclude),]
#only use neurocaps OCR subjects who also did tACS
idps_neurocaps_ocr <- idps_neurocaps_ocr[idps_neurocaps_ocr$id %in% idps_tacs$id,]
#only use tacs subjects with good neurocaps data
idps_tacs <- idps_tacs[idps_tacs$id %in% idps_neurocaps_ocr$id,]
#idps_tacs$dcr_tpre_response_craving_[0-7]
#idp_tdcs
#dcr_tpre_response_box_rt_0
#dcr_tpre_response_craving_rt_0
#idps_neurocaps_ocr$dcr_response_box_craving[0-7]
n_tdcs <- length(unique(idps_tdcs$id))
n_neurocaps_mcr <- length(unique(idps_neurocaps_mcr$id))
n_neurocaps_ocr <- length(unique(idps_neurocaps_ocr$id))
n_tacs <- length(unique(idps_tacs$id))
print('Subjects In Each Dataset')## [1] "Subjects In Each Dataset"print('tDCS MCR')## [1] "tDCS MCR"print(n_tdcs)## [1] 65print('Neurocaps MCR')## [1] "Neurocaps MCR"print(n_neurocaps_mcr)## [1] 29print('Neurocaps OCR')## [1] "Neurocaps OCR"print(n_neurocaps_ocr)## [1] 22print('tACS OCR')## [1] "tACS OCR"print(n_tacs)## [1] 22#write.csv(idps_tdcs$id, 'subjects-tdcs.csv', row.names = FALSE)
#write.csv(idps_neurocaps_mcr$id, 'subjects-neurocapsMCR.csv', row.names = FALSE)
#write.csv(idps_neurocaps_ocr$id, 'subjects-neurocapsOCR.csv', row.names = FALSE)
#write.csv(idps_tacs$id, 'subjects-tacs.csv', row.names = FALSE)
#write out behavioral data to share with Hamed/Henry
behavioral_cols <- grepl('response', names(idps_tdcs))
behavioral_cols[names(idps_tdcs) == 'id'] <- TRUE
#write.csv(idps_tdcs[, behavioral_cols], 'behavior-tdcs.csv', row.names = FALSE)
behavioral_cols <- grepl('tpre_response', names(idps_tacs))
behavioral_cols[names(idps_tacs) == 'id'] <- TRUE
#write.csv(idps_tacs[, behavioral_cols], 'behavior-tacs.csv', row.names = FALSE)
behavioral_cols <- grepl('response', names(idps_neurocaps_mcr))
behavioral_cols[names(idps_neurocaps_mcr) == 'id'] <- TRUE
#write.csv(idps_neurocaps_mcr[, behavioral_cols], 'behavior-neurocapsMCR.csv', row.names = FALSE)
behavioral_cols <- grepl('response', names(idps_neurocaps_ocr))
behavioral_cols[names(idps_neurocaps_ocr) == 'id'] <- TRUE
#write.csv(idps_neurocaps_ocr[, behavioral_cols], 'behavior-neurocapsOCR.csv', row.names = FALSE)
#idps_tacs <- read.csv('idps-tacs-abstract.csv')
#variable names are:
#for tACS
#dcr_tpre_stats_tdcsprelim_[condition].[run].0.coef_mean_[roi]
#for neurocaps
#dcr_stats_tdcsprelim_[condition].[run].0.coef_mean_[roi]
#conditions are: drug or neutral
#runs are (e.g. run 1 block 1):
#r11, r12, r13, r14
#ROIs are:
#1: VMPFC extracted at p < 0.001
#3: LSTG extracted at p < 0.001
#4: RSTG extracted at p < 0.001
#5: L Ventral Striatum extracted at p < 0.05 intersected with brainnetome ROIs 219 and 223
#6: R Ventral Striatum extracted at p < 0.05 intersected with brainnetome ROIs 220 and 224
#8: R Amygdala extracted at p < 0.05 intersected with brainnetome ROIs 212 and 214
library(reshape2)
library(lme4)## Loading required package: Matrixlibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(ggplot2)
library(sjstats) #for icc of mixed effects models
plot_one <- function(roi, this_label, idps, limits_spag, limits_y = c(-1, 1), prefix = 'dcr_', task = ''){
#roi <- '1'
#this_label <- 'VMPFC'
#roi <- '1'
#this_label <- 'VMPFC'
#prefix <- 'dcr_tpre_'
#idps <- idps_tdcs
column_prefixes <- c('stats_tdcsprelim_drug.r11.0.coef_mean_',
'stats_tdcsprelim_drug.r12.0.coef_mean_',
'stats_tdcsprelim_drug.r13.0.coef_mean_',
'stats_tdcsprelim_drug.r14.0.coef_mean_',
'stats_tdcsprelim_neutral.r11.0.coef_mean_',
'stats_tdcsprelim_neutral.r12.0.coef_mean_',
'stats_tdcsprelim_neutral.r13.0.coef_mean_',
'stats_tdcsprelim_neutral.r14.0.coef_mean_')
this_roi <- c('id', 'motion', paste0(prefix, column_prefixes, roi))
one_dataset <- idps[, this_roi]
long_data <- melt(one_dataset, id.vars = c('id', 'motion'))
long_data$condition <- NA
long_data$condition[grepl('neutral', long_data$variable)] <- 'neutral'
long_data$condition[grepl('drug', long_data$variable)] <- 'drug'
#put neutral in the intercept
long_data$condition <- factor(long_data$condition, levels = c('neutral', 'drug'))
long_data$time <- NA
long_data$time[grepl('r11', long_data$variable)] <- 1
long_data$time[grepl('r12', long_data$variable)] <- 2
long_data$time[grepl('r13', long_data$variable)] <- 3
long_data$time[grepl('r14', long_data$variable)] <- 4
#mean center on time
long_data$time <- long_data$time - mean(long_data$time)
#this_lme <- lmer(paste('value ~ condition * time + (1|id)'), data = long_data)
#for checking for NA's, looks like it's all good now
#print(long_data)
this_lme <- lmer(paste('value ~ condition * time + motion + (1|id/condition)'), data = long_data)
#print(summary(this_lme))
within_visit_iccs <- sjstats::icc(this_lme)
n_subjects <- length(unique(long_data$id))
means <- aggregate(long_data$value, by = list(long_data$condition, long_data$time), FUN = mean, na.rm = TRUE)
names(means)[1:2] <- c('condition', 'time')
sds <- aggregate(long_data$value, by = list(long_data$condition, long_data$time), FUN = sd, na.rm = TRUE)
names(sds)[1:2] <- c('condition', 'time')
#print(this_label)
mean_str <- paste(this_label, '_mean', sep = '')
names(means)[3] <- mean_str
sd_str <- paste(this_label, '_sd', sep = '')
se_str <- paste(this_label, '_se', sep = '')
names(sds)[3] <- sd_str
plot_frame <- merge(means, sds)
plot_frame[, se_str] <- plot_frame[, sd_str] / sqrt(n_subjects)
plot_frame$bar_top <- plot_frame[, mean_str] + plot_frame[, se_str]
plot_frame$bar_bottom <- plot_frame[, mean_str] - plot_frame[, se_str]
plot_frame$label <- this_label
ebsize <- 1.5
linesize <- 1
#swap condition levels back for this plot, so that drug is red and neutral is blue
plot_frame$condition <- factor(plot_frame$condition, levels = c('drug', 'neutral'))
#add 2.5 so time is blocks, uncentered
p <- ggplot(plot_frame) + geom_hline(yintercept = 0, color = 'gray') + geom_line(aes_string(x = 'time + 2.5', y = mean_str, color = 'condition'), size = linesize) +
geom_errorbar(aes_string(x = 'time + 2.5', ymax = 'bar_top', ymin = 'bar_bottom', color = 'condition', width = 0.1), size = ebsize) +
theme(text = element_text(size=10)) + ggtitle(task) + ylab('Percent Signal Change') + xlab('Time (blocks)') + ylim(limits_y)
#print(p)
p2 <- ggplot(data = long_data[long_data$condition == 'drug',], aes_string(x = 'time', y = 'value', group = 'id')) +
geom_line() + stat_summary(aes(group = 1), geom = "point", fun.y = mean, shape = 17, size = 3) +
stat_smooth(aes(group = 1)) + labs(
x = "Time",
y = "% Signal Change",
title = "drug")
#print(p2)
p3 <- ggplot(data = long_data[long_data$condition == 'neutral',], aes_string(x = 'time', y = 'value', group = 'id')) +
geom_line() + stat_summary(aes(group = 1), geom = "point", fun.y = mean, shape = 17, size = 3) +
stat_smooth(aes(group = 1)) + labs(
x = "Time",
y = "% Signal Change",
title = "neutral")
#print(p3)
wide_icc_data <- dcast(long_data, id ~ variable)
within_iccs_simple <- ICC(wide_icc_data[, 2:ncol(wide_icc_data)])
within_iccs_simple_intervals <- extract_icc_intervals(within_iccs_simple)
wide_icc_data_contrasts <- dcast(long_data, id + time ~ condition)
wide_icc_data_contrasts$DrugvNeutral <- wide_icc_data_contrasts$drug - wide_icc_data_contrasts$neutral
contrast_icc_data <- dcast(wide_icc_data_contrasts, id ~ time, value.var = 'DrugvNeutral')
contrast_iccs <- ICC(contrast_icc_data[, 2:ncol(contrast_icc_data)])
within_contrast_icc_intervals <- extract_icc_intervals(contrast_iccs)
return(list(model = this_lme, plotframe = plot_frame, p = p, p2=p2, p3=p3, dset = long_data, within_visit_iccs = within_visit_iccs,
within_iccs_simple_intervals = within_iccs_simple_intervals,
within_contrast_icc_intervals = within_contrast_icc_intervals, task = task))
}
plot_one_beh <- function(measure, this_label, idps, limits_spag, limits_y = c(1, 4), prefix = 'dcr_', task = ''){
#same as above, but for behavioral data
#prefix will be dcr_tpre_ for tdcs/tacs and dcr_ for neurocaps
#measure will be box_rt, craving_rt, or craving
#measure <- 'craving'
#prefix <- 'dcr_tpre_'
#this_label <- 'Craving'
#idps <- idps_tdcs
#idps_tacs$dcr_tpre_response_craving_[0-7]
#idp_tdcs
#dcr_tpre_response_box_rt_0
#dcr_tpre_response_craving_rt_0
#idps_neurocaps_ocr$dcr_response_box_craving[0-7]
column_suffixes <- 0:7
these_cols <- c('id', paste0(prefix, 'response_', measure, '_', column_suffixes))
library(reshape2)
one_dataset <- idps[, these_cols]
long_data <- melt(one_dataset, id.vars = c('id'))
long_data$variable <- as.character(long_data$variable)
long_data$condition <- NA
long_data$number <- substr(long_data$variable, nchar(long_data$variable), nchar(long_data$variable))
long_data$condition[long_data$number %in% c('0', '2', '4', '6')] <- 'neutral'
long_data$condition[long_data$number %in% c('1', '3', '5', '7')] <- 'drug'
#put neutral in the intercept
long_data$condition <- factor(long_data$condition, levels = c('neutral', 'drug'))
#time = block number, just like for the imaging variables
long_data$time <- NA
long_data$time[long_data$number %in% c('0', '1')] <- 1
long_data$time[long_data$number %in% c('2', '3')] <- 2
long_data$time[long_data$number %in% c('4', '5')] <- 3
long_data$time[long_data$number %in% c('6', '7')] <- 4
#mean center on time
long_data$time <- long_data$time - mean(long_data$time)
library(lme4)
library(lmerTest)
library(ggplot2)
library(sjstats) #for icc of mixed effects models
#this_lme <- lmer(paste('value ~ condition * time + (1|id)'), data = long_data)
#for checking for NA's, looks like it's all good now
#print(long_data)
this_lme <- lmer(paste('value ~ condition * time + (1|id/condition)'), data = long_data)
#print(summary(this_lme))
within_visit_iccs <- sjstats::icc(this_lme)
n_subjects <- length(unique(long_data$id))
means <- aggregate(long_data$value, by = list(long_data$condition, long_data$time), FUN = mean, na.rm = TRUE)
names(means)[1:2] <- c('condition', 'time')
sds <- aggregate(long_data$value, by = list(long_data$condition, long_data$time), FUN = sd, na.rm = TRUE)
names(sds)[1:2] <- c('condition', 'time')
#print(this_label)
mean_str <- paste(this_label, '_mean', sep = '')
names(means)[3] <- mean_str
sd_str <- paste(this_label, '_sd', sep = '')
se_str <- paste(this_label, '_se', sep = '')
names(sds)[3] <- sd_str
plot_frame <- merge(means, sds)
plot_frame[, se_str] <- plot_frame[, sd_str] / sqrt(n_subjects)
plot_frame$bar_top <- plot_frame[, mean_str] + plot_frame[, se_str]
plot_frame$bar_bottom <- plot_frame[, mean_str] - plot_frame[, se_str]
plot_frame$label <- this_label
ebsize <- 1.5
linesize <- 1
#swap condition levels back for this plot, so that drug is red and neutral is blue
plot_frame$condition <- factor(plot_frame$condition, levels = c('drug', 'neutral'))
p <- ggplot(plot_frame) + geom_line(aes_string(x = 'time + 2.5', y = mean_str, color = 'condition'), size = linesize) + geom_errorbar(aes_string(x = 'time + 2.5', ymax = 'bar_top', ymin = 'bar_bottom', color = 'condition', width = 0.1), size = ebsize) +
theme(text = element_text(size=10)) + ggtitle(task) + ylab('') + xlab('') + ylim(limits_y)
#print(p)
p2 <- ggplot(data = long_data[long_data$condition == 'drug',], aes_string(x = 'time', y = 'value', group = 'id')) +
geom_line() + stat_summary(aes(group = 1), geom = "point", fun.y = mean, shape = 17, size = 3) +
stat_smooth(aes(group = 1)) + labs(
x = "Time",
y = this_label,
title = "drug")
#print(p2)
p3 <- ggplot(data = long_data[long_data$condition == 'neutral',], aes_string(x = 'time', y = 'value', group = 'id')) +
geom_line() + stat_summary(aes(group = 1), geom = "point", fun.y = mean, shape = 17, size = 3) +
stat_smooth(aes(group = 1)) + labs(
x = "Time",
y = this_label,
title = "neutral")
#print(p3)
wide_icc_data <- dcast(long_data, id ~ variable)
within_iccs_simple <- ICC(wide_icc_data[, 2:ncol(wide_icc_data)])
within_iccs_simple_intervals <- extract_icc_intervals(within_iccs_simple)
wide_icc_data_contrasts <- dcast(long_data, id + time ~ condition)
wide_icc_data_contrasts$DrugvNeutral <- wide_icc_data_contrasts$drug - wide_icc_data_contrasts$neutral
contrast_icc_data <- dcast(wide_icc_data_contrasts, id ~ time, value.var = 'DrugvNeutral')
contrast_iccs <- ICC(contrast_icc_data[, 2:ncol(contrast_icc_data)])
within_contrast_icc_intervals <- extract_icc_intervals(contrast_iccs)
return(list(model = this_lme, plotframe = plot_frame, p = p, p2=p2, p3=p3, dset = long_data, within_visit_iccs = within_visit_iccs,
within_iccs_simple_intervals = within_iccs_simple_intervals,
within_contrast_icc_intervals = within_contrast_icc_intervals, task = task))
}
library(sjPlot)
library(grid)
library(gridExtra)
library(ggpubr)## Loading required package: magrittrlibrary(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:sjstats':
##
## pca, phi## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha#condition by time interaction
#1: VMPFC extracted at p < 0.001
#3: LSTG extracted at p < 0.001
#4: RSTG extracted at p < 0.001
#5: L Ventral Striatum extracted at p < 0.05 intersected with brainnetome ROIs 219 and 223
#6: R Ventral Striatum extracted at p < 0.05 intersected with brainnetome ROIs 220 and 224
#8: R Amygdala extracted at p < 0.05 intersected with brainnetome ROIs 212 and 214
#main effect of condition
#9: LIFG extracted at p < 0.001
#10: RIFG at p < 0.001
#12: RDLPFC at p < 0.001
extract_icc_intervals <- function(this_icc){
single_absolute <- round(this_icc$results$ICC[1], 3)
single_absolute_lower <- this_icc$results$`lower bound`[1]
single_absolute_upper <- this_icc$results$`upper bound`[1]
single_fixed <- round(this_icc$results$ICC[3], 3)
single_fixed_lower <- this_icc$results$`lower bound`[3]
single_fixed_upper <- this_icc$results$`upper bound`[3]
average_fixed <- round(this_icc$results$ICC[6], 3)
average_fixed_lower <- this_icc$results$`lower bound`[6]
average_fixed_upper <- this_icc$results$`upper bound`[6]
this_row <- data.frame(t(c(ICC1 = single_absolute, ICC1_lower = single_absolute_lower, ICC1_upper = single_absolute_upper, ICC3 = single_fixed,
ICC3_lower = single_fixed_lower, ICC3_upper = single_fixed_upper,
ICC3k = average_fixed, ICC3k_lower = average_fixed_lower, ICC3k_upper = average_fixed_upper)))
return(this_row)
}
compare_models <- function(tdcs_model, neurocaps_mcr_model, neurocaps_ocr_model, tacs_model, label){
#prints off tables of z-scores and p-values for the coefficients of condition and condition*time compared between all pairs of models
#tdcs_summary <- summary(tdcs_model)
#fill this with z-scores testing for differences between coefficients of different models
ztable_condition <- data.frame(tdcs=c(NA,NA,NA,NA), neurocaps_mcr = c(NA,NA,NA,NA),
neurocaps_ocr = c(NA,NA,NA,NA), tacs=c(NA,NA,NA,NA))
rownames(ztable_condition) <- c('tdcs', 'neurocaps_mcr', 'neurocaps_ocr', 'tacs')
ztable_conditionbytime <- data.frame(tdcs=c(NA,NA,NA,NA), neurocaps_mcr = c(NA,NA,NA,NA),
neurocaps_ocr = c(NA,NA,NA,NA), tacs=c(NA,NA,NA,NA))
rownames(ztable_conditionbytime) <- c('tdcs', 'neurocaps_mcr', 'neurocaps_ocr', 'tacs')
all_models <- list(summary(tdcs_model), summary(neurocaps_mcr_model), summary(neurocaps_ocr_model), summary(tacs_model))
for (i in 1:3){
for (j in (i+1):4){
#condition beta
beta1 <- all_models[[i]]$coefficients[2,1]
#condition SE
se1 <- all_models[[i]]$coefficients[2,2]
#condition beta
beta2 <- all_models[[j]]$coefficients[2,1]
#condition SE
se2 <- all_models[[j]]$coefficients[2,2]
this_z <- (beta1 - beta2) / sqrt(se1 * se1 + se2 * se2)
ztable_condition[i,j] <- ztable_condition[j,i] <- this_z
#conditionbytime beta--row 5 for imaging and 4 for behavioral (since motion is not included there)--but always the last row
amodel <- all_models[[i]]$coefficients
cbytime_idx <- nrow(amodel)
beta1 <- all_models[[i]]$coefficients[cbytime_idx,1]
#conditionbytime SE
se1 <- all_models[[i]]$coefficients[cbytime_idx,2]
#conditionbytime beta
beta2 <- all_models[[j]]$coefficients[cbytime_idx,1]
#conditionbytime SE
se2 <- all_models[[j]]$coefficients[cbytime_idx,2]
this_z <- (beta1 - beta2) / sqrt(se1 * se1 + se2 * se2)
ztable_conditionbytime[i,j] <- ztable_conditionbytime[j,i] <- this_z
}
}
ptable_condition <- ztable_condition
ptable_conditionbytime <- ztable_conditionbytime
for (n in names(ptable_condition)){
ptable_condition[,n] <- 2*pnorm(-abs(ptable_condition[,n]))
ptable_conditionbytime[,n] <- 2*pnorm(-abs(ptable_conditionbytime[,n]))
}
print('###Condition z-scores###')
print(ztable_condition)
print('###Condition by time z-scores###')
print(ztable_conditionbytime)
print('###Condition p-values###')
print(ptable_condition)
print('###Condition by time p-values###')
print(ptable_conditionbytime)
write.csv(ztable_condition, paste0('ztable-condition-', label, '.csv'), row.names = FALSE)
write.csv(ztable_conditionbytime, paste0('ztable-conditionbytime-', label, '.csv'), row.names = FALSE)
write.csv(ptable_condition, paste0('ptable-condition-', label, '.csv'), row.names = FALSE)
write.csv(ptable_conditionbytime, paste0('ptable-conditionbytime-', label, '.csv'), row.names = FALSE)
}
##taken from jsPlot/color_utils.R to matchbe able to match colors easily
col_check2 <- function(geom.colors, collen) {
# --------------------------------------------
# check color argument
# --------------------------------------------
# check for corrct color argument
if (!is.null(geom.colors)) {
# check for color brewer palette
if (is.brewer.pal(geom.colors[1])) {
geom.colors <- scales::brewer_pal(palette = geom.colors[1])(collen)
} else if (is.sjplot.pal(geom.colors[1])) {
geom.colors <- get_sjplot_colorpalette(geom.colors[1], collen)
# do we have correct amount of colours?
} else if (geom.colors[1] == "gs") {
geom.colors <- scales::grey_pal()(collen)
# do we have correct amount of colours?
} else if (geom.colors[1] == "bw") {
geom.colors <- rep("black", times = collen)
# do we have correct amount of colours?
} else if (length(geom.colors) > collen) {
# shorten palette
geom.colors <- geom.colors[1:collen]
} else if (length(geom.colors) < collen) {
# repeat color palette
geom.colors <- rep(geom.colors, times = collen)
# shorten to required length
geom.colors <- geom.colors[1:collen]
}
} else {
geom.colors <- scales::brewer_pal(palette = "Set1")(collen)
}
geom.colors
}
# check whether a color value is indicating
# a color brewer palette
is.brewer.pal <- function(pal) {
bp.seq <- c("BuGn", "BuPu", "GnBu", "OrRd", "PuBu", "PuBuGn", "PuRd", "RdPu",
"YlGn", "YlGnBu", "YlOrBr", "YlOrRd", "Blues", "Greens", "Greys",
"Oranges", "Purples", "Reds")
bp.div <- c("BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu",
"RdYlGn", "Spectral")
bp.qul <- c("Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1",
"Set2", "Set3")
bp <- c(bp.seq, bp.div, bp.qul)
pal %in% bp
}
plot_combination <- function(roi, label, limits_spag = c(-2,2), limits_y = c(-1, 1), imaging = TRUE, forest_range = c(-0.5, 0.5)){
#roi <- '9'
#label <- 'LIFG'
#limits_y = c(-0.3, 0.5)
#limits_spag = c(-2, 2)
#imaging = TRUE
if (imaging){
tdcs_list <- plot_one(roi, label, idps_tdcs, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_tpre_', task = 'Discovery Sample')
neurocaps_mcr_list <- plot_one(roi, label, idps_neurocaps_mcr, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_', task = 'Replication Sample 1')
neurocaps_ocr_list <- plot_one(roi, label, idps_neurocaps_ocr, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_', task = 'Replication Sample 2')
tacs_list <- plot_one(roi, label, idps_tacs, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_tpre_', task = 'Sample 2-Retest')
} else {
tdcs_list <- plot_one_beh(roi, label, idps_tdcs, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_tpre_', task = 'Discovery Sample')
neurocaps_mcr_list <- plot_one_beh(roi, label, idps_neurocaps_mcr, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_', task = 'Replication Sample 1')
neurocaps_ocr_list <- plot_one_beh(roi, label, idps_neurocaps_ocr, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_', task = 'Replication Sample 2')
tacs_list <- plot_one_beh(roi, label, idps_tacs, limits_spag = limits_spag, limits_y = limits_y, prefix = 'dcr_tpre_', task = 'Sample 2-Retest')
}
compare_models(tdcs_list$model, neurocaps_mcr_list$model, neurocaps_ocr_list$model, tacs_list$model, label)
###
#extract betas and SEs for contrasts, to compare with finer grained method
plot_frame <- NULL
for(this_list in list(tdcs_list, neurocaps_mcr_list, neurocaps_ocr_list, tacs_list)){
print('one')
s1 <- summary(this_list$model)
condition_beta <- s1$coefficients[, 'Estimate']['conditiondrug']
conditionxtime_beta <- s1$coefficients[, 'Estimate']['conditiondrug:time']
condition_se <- s1$coefficients[, 'Std. Error']['conditiondrug']
conditionxtime_se <- s1$coefficients[, 'Std. Error']['conditiondrug:time']
tr_beta <- s1$coefficients[, 'Estimate']['time']
tr_se <- s1$coefficients[, 'Std. Error']['time']
plot_row <- data.frame(beta = c(condition_beta, conditionxtime_beta, tr_beta), se = c(condition_se, conditionxtime_se, tr_se),
effect = c('Drug>Neutral', 'Interaction', 'Time'), task = this_list$task)
plot_frame <- rbind(plot_frame, plot_row)
}
#ggplot(plot_frame) + geom_point(aes(y = beta, x = effect))
plot_frame$lower = plot_frame$beta - 1.96*plot_frame$se
plot_frame$upper = plot_frame$beta + 1.96*plot_frame$se
#plot_frame$task <- factor(plot_frame$task, levels = c('Discovery Sample', 'Replication Sample 1', 'Replication Sample 2', 'Sample 2-Retest'))
plot_frame$task <- factor(plot_frame$task, levels = c('Sample 2-Retest', 'Replication Sample 2','Replication Sample 1', 'Discovery Sample'))
#plot_frame$task <- factor(plot_frame$task, levels = c('1212OCR2', '1212OCR','Replication Sample 1', '1212MCR'))
plot_frame$effect <- factor(plot_frame$effect, levels = c('Drug>Neutral', 'Time', 'Interaction'))
p = ggplot(data=plot_frame,
aes(x = task,y = beta, ymin = lower, ymax = upper ))+
geom_pointrange(aes(col=task))+
geom_hline(aes(fill=task),yintercept =0, linetype=2)+
xlab(label)+ ylab("Contrast Beta (95% Confidence Interval)")+
geom_errorbar(aes(ymin=lower, ymax=upper,col=task),width=0.5,cex=1)+
facet_wrap(~effect,strip.position="left",nrow=3,scales = "free_y") +
theme(plot.title=element_text(size=16,face="bold"),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_text(face="bold"),
axis.title=element_text(size=12,face="bold"),
strip.text.y = element_text(hjust=0,vjust = 1,angle=180,face="bold"))+
coord_flip() + scale_colour_manual(values = col_check2('Set1', 4))
print(p)
#save the plot_frame, so we can easily plot a summary of old vs new analysis
write.csv(plot_frame, paste0('plot_frame-', label, '.csv'), row.names = TRUE)
###
#print model with restricted range
print(plot_models(tdcs_list$model, neurocaps_mcr_list$model, neurocaps_ocr_list$model, tacs_list$model, m.labels = c('tDCS', 'Replication Sample 1', '1212OCR', '1212OCR2')) + ylim(forest_range))
#plot model without restricting range so you can see where 'motion' falls
print(plot_models(tdcs_list$model, neurocaps_mcr_list$model, neurocaps_ocr_list$model, tacs_list$model, m.labels = c('tDCS', 'Replication Sample 1', '1212OCR', '1212OCR2')))# + ylim(-0.5, 0.5))
#from:
#https://stats.stackexchange.com/questions/93540/testing-equality-of-coefficients-from-two-different-regressions
#to test for a difference in betas, compute:
#Z = (beta1 - beta2)/sqrt(SE(beta1)^2 + SE(beta2)^2)
pwidth = 6
pheight = 6
print(ggarrange(tdcs_list$p, neurocaps_mcr_list$p, neurocaps_ocr_list$p, tacs_list$p, ncol = 2, nrow = 2, common.legend = TRUE, legend = 'bottom'))
ggsave(paste0(label, '-lineplot.png') , ggarrange(tdcs_list$p, neurocaps_mcr_list$p, neurocaps_ocr_list$p, tacs_list$p, ncol = 2, nrow = 2,
common.legend = TRUE, legend = 'bottom'), width = pwidth, height = pheight)
print(ggarrange(tdcs_list$p2, neurocaps_mcr_list$p2, neurocaps_ocr_list$p2, tacs_list$p2, ncol = 2, nrow = 2, common.legend = TRUE, legend = 'bottom'))
print(ggarrange(tdcs_list$p3, neurocaps_mcr_list$p3, neurocaps_ocr_list$p3, tacs_list$p3, ncol = 2, nrow = 2, common.legend = TRUE, legend = 'bottom'))
#get ICCs for each of the 8 conditions
icc_data <- merge(neurocaps_ocr_list$dset, tacs_list$dset, by = c('id', 'time', 'condition'))
all_iccs <- NULL
mean_values <- NA
for (con in unique(icc_data$condition)){
for (tim in unique(icc_data$time)){
for_icc <- icc_data[icc_data$time == tim & icc_data$condition == con, c('value.x', 'value.y')]
this_icc <- ICC(for_icc)
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste(tim, con)
all_iccs <- rbind(all_iccs, this_row)
#get values for drug and neutral at this time point to put into mean_values frame
for_means <- icc_data[icc_data$time == tim & icc_data$condition == con, c('id', 'value.x', 'value.y')]
value_cols <- paste0(con, tim, c('NeuroCaps', 'tACS'))
names(for_means) <- c('id', value_cols)
mean_values <- merge(mean_values, for_means, all = TRUE)
}
#get ICCs for mean of drug and neutral
for_icc <- icc_data[icc_data$condition == con, c('id', 'value.x', 'value.y')]
for_icc <- aggregate(for_icc[, c('value.x', 'value.y')], by = list(for_icc$id), FUN = mean)
value_cols <- paste0(con, c('NeuroCaps', 'tACS'))
names(for_icc) <- c('id', value_cols)
mean_values <- merge(mean_values, for_icc, all = TRUE)
this_icc <- ICC(for_icc[, value_cols])
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste(con)
all_iccs <- rbind(all_iccs, this_row)
}
for (tim in unique(icc_data$time)){
neurocaps_colname <- paste0('NeuroCapsDvN', tim)
tacs_colname <- paste0('tACSDvN', tim)
mean_values[, neurocaps_colname] <- mean_values[, paste0('drug', tim, 'NeuroCaps')] - mean_values[, paste0('neutral', tim, 'NeuroCaps')]
mean_values[, tacs_colname] <- mean_values[, paste0('drug', tim, 'tACS')] - mean_values[, paste0('neutral', tim, 'tACS')]
this_icc <- ICC(mean_values[, c(neurocaps_colname, tacs_colname)])
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste0(tim, 'DrugvNeutral')
all_iccs <- rbind(all_iccs, this_row)
}
#get early and late mean values
mean_values$NeuroCapsDvNEarly <- (mean_values$`NeuroCapsDvN-1.5` + mean_values$`NeuroCapsDvN-0.5`) / 2
mean_values$NeuroCapsDvNLate <- (mean_values$`NeuroCapsDvN1.5` + mean_values$`NeuroCapsDvN0.5`) / 2
mean_values$tACSDvNEarly <- (mean_values$`tACSDvN-1.5` + mean_values$`tACSDvN-0.5`) / 2
mean_values$tACSDvNLate <- (mean_values$`tACSDvN1.5` + mean_values$`tACSDvN0.5`) / 2
#compute early and late ICCs
this_icc <- ICC(mean_values[, c('NeuroCapsDvNEarly', 'tACSDvNEarly')])
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste('DrugvNeutralEarly')
all_iccs <- rbind(all_iccs, this_row)
this_icc <- ICC(mean_values[, c('NeuroCapsDvNLate', 'tACSDvNLate')])
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste('DrugvNeutralLate')
all_iccs <- rbind(all_iccs, this_row)
mean_values$NeuroCapsDvN <- mean_values$drugNeuroCaps - mean_values$neutralNeuroCaps
mean_values$tACSDvN <- mean_values$drugtACS - mean_values$neutraltACS
this_icc <- ICC(mean_values[, c('NeuroCapsDvN', 'tACSDvN')])
this_row <- extract_icc_intervals(this_icc)
this_row$var <- paste('DrugvNeutral')
all_iccs <- rbind(all_iccs, this_row)
#get ICCs for mean contrast of drug and neutral in each block and in blocks 1/2 and 3/4 separately
melted_iccs <- all_iccs[, c('var', 'ICC1', 'ICC1_lower', 'ICC1_upper')]
names(melted_iccs) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs$variable <- 'ICC1'
melted_iccs3 <- all_iccs[, c('var', 'ICC3', 'ICC3_lower', 'ICC3_upper')]
names(melted_iccs3) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs3$variable <- 'ICC3'
melted_iccs <- rbind(melted_iccs, melted_iccs3)
#melted_iccs$var <- factor(melted_iccs$var, levels = rev(to_plot))
melted_iccs$var <- factor(melted_iccs$var, levels = c('DrugvNeutral', 'drug', 'neutral',
"DrugvNeutralLate", "DrugvNeutralEarly",
"-1.5DrugvNeutral", "-0.5DrugvNeutral", "0.5DrugvNeutral", "1.5DrugvNeutral",
'1.5 drug', '0.5 drug', '-0.5 drug', '-1.5 drug',
'1.5 neutral', '0.5 neutral', '-0.5 neutral', '-1.5 neutral'))
p <- ggplot(melted_iccs) + geom_bar(aes(x = var, fill = variable, y = ICC), stat = 'identity', position = 'dodge') + coord_flip() +
xlab('Variable') + ylab('ICC') + ggtitle(paste0('Between Session: ', label)) + geom_errorbar(aes(x = var, fill = variable, ymin = lower, ymax = upper), stat = 'identity', position = 'dodge')
print(p)
print(all_iccs)
ggsave(paste0('ICCs_', label, '.png'), p)
#plot ICCs that come from the LME
within_icc_results <- as.data.frame(rbind(tdcs_list$within_visit_iccs, neurocaps_mcr_list$within_visit_iccs, neurocaps_ocr_list$within_visit_iccs, tacs_list$within_visit_iccs))
within_icc_results$dset <- c('tDCS', 'WirMCR', '1212OCR', '1212OCR2')
within_icc_results$id_condition <- within_icc_results$id + within_icc_results$`condition:id`
melted_within_data <- melt(within_icc_results, id.vars = c('dset', 'condition:id'))
melted_within_data$dset <- factor(melted_within_data$dset, levels = c('1212OCR', '1212OCR2','WirMCR', 'tDCS'))
p <- ggplot(data = melted_within_data) + geom_bar(aes(x = dset, y = value, color = variable, fill = variable), stat = 'identity', position = 'dodge') +
coord_flip() + ggtitle(paste0('Within Session ICCs from LME for: ', label))
print(p)
#plot within-session ICCs from psych's ICC function, treating all conditions as the same (should be similar to 'id' above)
tdcs_list$within_iccs_simple_intervals$var <- 'tDCS'
all_iccs <- tdcs_list$within_iccs_simple_intervals
neurocaps_mcr_list$within_iccs_simple_intervals$var <- 'WirMCR'
all_iccs <- rbind(all_iccs, neurocaps_mcr_list$within_iccs_simple_intervals)
neurocaps_ocr_list$within_iccs_simple_intervals$var <- '1212OCR'
all_iccs <- rbind(all_iccs, neurocaps_ocr_list$within_iccs_simple_intervals)
tacs_list$within_iccs_simple_intervals$var <- '1212OCR2'
all_iccs <- rbind(all_iccs, tacs_list$within_iccs_simple_intervals)
###plot--should functionalize this
melted_iccs <- all_iccs[, c('var', 'ICC1', 'ICC1_lower', 'ICC1_upper')]
names(melted_iccs) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs$variable <- 'ICC1'
melted_iccs3 <- all_iccs[, c('var', 'ICC3', 'ICC3_lower', 'ICC3_upper')]
names(melted_iccs3) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs3$variable <- 'ICC3'
melted_iccs3k <- all_iccs[, c('var', 'ICC3k', 'ICC3k_lower', 'ICC3k_upper')]
names(melted_iccs3k) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs3k$variable <- 'ICC3k'
melted_iccs <- rbind(melted_iccs, melted_iccs3)
melted_iccs <- rbind(melted_iccs, melted_iccs3k)
#melted_iccs$var <- factor(melted_iccs$var, levels = rev(to_plot))
melted_iccs$var <- factor(melted_iccs$var, levels = c('1212OCR2', '1212OCR','WirMCR', 'tDCS'))
p <- ggplot(melted_iccs) + geom_bar(aes(x = var, fill = variable, y = ICC), stat = 'identity', position = 'dodge') + coord_flip() +
xlab('Variable') + ylab('ICC') + ggtitle(paste0('Within Session Simple ICC: ', label)) + geom_errorbar(aes(x = var, fill = variable, ymin = lower, ymax = upper), stat = 'identity', position = 'dodge')
print(p)
#plot within-session ICCs from psych's ICC function, using contrasts
tdcs_list$within_contrast_icc_intervals$var <- 'tDCS'
all_iccs <- tdcs_list$within_contrast_icc_intervals
neurocaps_mcr_list$within_contrast_icc_intervals$var <- 'WirMCR'
all_iccs <- rbind(all_iccs, neurocaps_mcr_list$within_contrast_icc_intervals)
neurocaps_ocr_list$within_contrast_icc_intervals$var <- '1212OCR'
all_iccs <- rbind(all_iccs, neurocaps_ocr_list$within_contrast_icc_intervals)
tacs_list$within_contrast_icc_intervals$var <- '1212OCR2'
all_iccs <- rbind(all_iccs, tacs_list$within_contrast_icc_intervals)
###plot--should functionalize this
melted_iccs <- all_iccs[, c('var', 'ICC1', 'ICC1_lower', 'ICC1_upper')]
names(melted_iccs) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs$variable <- 'ICC1'
melted_iccs3 <- all_iccs[, c('var', 'ICC3', 'ICC3_lower', 'ICC3_upper')]
names(melted_iccs3) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs3$variable <- 'ICC3'
melted_iccs3k <- all_iccs[, c('var', 'ICC3k', 'ICC3k_lower', 'ICC3k_upper')]
names(melted_iccs3k) <- c('var', 'ICC', 'lower', 'upper')
melted_iccs3k$variable <- 'ICC3k'
melted_iccs <- rbind(melted_iccs, melted_iccs3)
melted_iccs <- rbind(melted_iccs, melted_iccs3k)
#melted_iccs$var <- factor(melted_iccs$var, levels = rev(to_plot))
melted_iccs$var <- factor(melted_iccs$var, levels = c('1212OCR2', '1212OCR','WirMCR', 'tDCS'))
p <- ggplot(melted_iccs) + geom_bar(aes(x = var, fill = variable, y = ICC), stat = 'identity', position = 'dodge') + coord_flip() +
xlab('Variable') + ylab('ICC') + ggtitle(paste0('Within Session Contrast ICC: ', label)) + geom_errorbar(aes(x = var, fill = variable, ymin = lower, ymax = upper), stat = 'identity', position = 'dodge')
print(p)
###
print('###tDCS MCR###')
print(summary(tdcs_list$model))
#print(anova(tdcs_list$model, type = 'marginal'))
print('###Neurocaps MCR###')
print(summary(neurocaps_mcr_list$model))
print('###Neurocaps OCR###')
print(summary(neurocaps_ocr_list$model))
print('###tACS OCR###')
print(summary(tacs_list$model))
##make model output table to save
tablefile <- paste0(label, '-modeltable.csv')
write.table('tDCS Model Output', tablefile)
write.table(round(summary(tdcs_list$model)$coefficients, digits = 3), file = tablefile,
row.names = TRUE, append = TRUE, sep = ',')
write.table('Neurocaps MCR Model Output', tablefile, append = TRUE)
write.table(round(summary(neurocaps_mcr_list$model)$coefficients, digits = 3), file = tablefile,
row.names = TRUE, append = TRUE, sep = ',')
write.table('Neurocaps OCR Model Output', tablefile, append = TRUE)
write.table(round(summary(neurocaps_ocr_list$model)$coefficients, digits = 3), file = tablefile,
row.names = TRUE, append = TRUE, sep = ',')
write.table('tACS OCR Model Output', tablefile, append = TRUE)
write.table(round(summary(tacs_list$model)$coefficients, digits = 3), file = tablefile,
row.names = TRUE, append = TRUE, sep = ',')
}Condition
LIFG
knitr::include_graphics("LIFG_z16.png")
plot_combination('9', 'LIFG', limits_y = c(-0.15, 0.45))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 1.1616086 0.8054013 0.3833178
## neurocaps_mcr 1.1616086 NA -0.1122026 -0.5502508
## neurocaps_ocr 0.8054013 -0.1122026 NA -0.3762163
## tacs 0.3833178 -0.5502508 -0.3762163 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 1.222763 -0.3421076 -1.831715
## neurocaps_mcr 1.2227631 NA -1.2144775 -2.462810
## neurocaps_ocr -0.3421076 -1.214477 NA -1.190708
## tacs -1.8317149 -2.462810 -1.1907079 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.2453945 0.4205881 0.7014841
## neurocaps_mcr 0.2453945 NA 0.9106628 0.5821474
## neurocaps_ocr 0.4205881 0.9106628 NA 0.7067561
## tacs 0.7014841 0.5821474 0.7067561 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.22141917 0.7322699 0.06699391
## neurocaps_mcr 0.22141917 NA 0.2245654 0.01378528
## neurocaps_ocr 0.73226988 0.22456544 NA 0.23376828
## tacs 0.06699391 0.01378528 0.2337683 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 -0.011 -0.41613414 0.4015172 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5 drug
## 2 0.570 0.21179397 0.7944981 0.570 0.20462615 0.7956329 0.726 0.33973387 0.8861866 -1.5 drug
## 3 0.372 -0.04089754 0.6791548 0.385 -0.03340966 0.6887739 0.556 -0.06912889 0.8157088 0.5 drug
## 4 0.203 -0.22190266 0.5668081 0.203 -0.22901444 0.5688981 0.338 -0.59408230 0.7252200 1.5 drug
## 5 0.535 0.16369113 0.7753645 0.543 0.16742384 0.7810495 0.704 0.28682614 0.8770666 drug
## 6 0.060 -0.35530845 0.4597212 0.089 -0.33631783 0.4846977 0.164 -1.01349062 0.6529244 -0.5 neutral
## 7 0.383 -0.02882437 0.6856138 0.387 -0.03072059 0.6901861 0.559 -0.06338850 0.8166983 -1.5 neutral
## 8 0.069 -0.34750317 0.4667153 0.090 -0.33572472 0.4852091 0.165 -1.01079998 0.6533882 0.5 neutral
## 9 0.031 -0.38099588 0.4359664 0.066 -0.35686693 0.4666275 0.124 -1.10977631 0.6363272 1.5 neutral
## 10 0.219 -0.20686902 0.5774081 0.219 -0.21402982 0.5794607 0.359 -0.54462579 0.7337450 neutral
## 11 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.243 -0.18220152 0.5942463 0.243 -0.18943545 0.5962381 0.391 -0.46741607 0.7470541 -1.5DrugvNeutral
## 13 0.511 0.13178270 0.7620211 0.511 0.12441301 0.7633116 0.677 0.22129414 0.8657705 0.5DrugvNeutral
## 14 -0.032 -0.43372330 0.3833580 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5DrugvNeutral
## 15 0.238 -0.18665261 0.5912574 0.238 -0.19387401 0.5932602 0.385 -0.48100177 0.7447122 DrugvNeutralEarly
## 16 0.327 -0.09261566 0.6501636 0.329 -0.09801728 0.6531131 0.495 -0.21733738 0.7901614 DrugvNeutralLate
## 17 0.491 0.10440168 0.7501278 0.491 0.09698501 0.7514737 0.658 0.17682102 0.8581045 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 125.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9228 -0.5555 0.0131 0.5274 3.4993
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01771 0.1331
## Residual 0.06172 0.2484
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.232357 0.042932 71.923445 5.412 7.80e-07 ***
## conditiondrug 0.140665 0.021789 452.000000 6.456 2.78e-10 ***
## time 0.013784 0.013781 452.000000 1.000 0.31774
## motion -1.119903 0.356675 63.000000 -3.140 0.00257 **
## conditiondrug:time -0.003891 0.019489 452.000000 -0.200 0.84186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.254
## time 0.000 0.000
## motion -0.851 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 142.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8048 -0.4694 -0.0166 0.5285 3.8206
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.005082 0.07129
## id (Intercept) 0.022843 0.15114
## Residual 0.084911 0.29139
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05421 0.08188 30.90438 0.662 0.5128
## conditiondrug 0.08509 0.04260 28.00000 1.998 0.0556 .
## time 0.02114 0.02420 172.00000 0.874 0.3836
## motion -0.13958 0.71730 27.00000 -0.195 0.8472
## conditiondrug:time -0.05205 0.03422 172.00000 -1.521 0.1301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.260
## time 0.000 0.000
## motion -0.864 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 106.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7934 -0.5240 0.0839 0.5783 2.5766
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.012444 0.11155
## id (Intercept) 0.007477 0.08647
## Residual 0.084054 0.28992
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.07747 0.07432 26.25739 1.042 0.307
## conditiondrug 0.09291 0.05515 21.00000 1.685 0.107
## time 0.02024 0.02764 130.00000 0.732 0.465
## motion 0.30032 0.56748 20.00000 0.529 0.602
## conditiondrug:time 0.01105 0.03909 130.00000 0.283 0.778
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.371
## time 0.000 0.000
## motion -0.814 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 116.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1737 -0.5107 -0.0123 0.4421 2.9365
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.003373 0.05808
## id (Intercept) 0.028954 0.17016
## Residual 0.088214 0.29701
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01767 0.10289 22.30565 0.172 0.8652
## conditiondrug 0.12043 0.04808 21.00000 2.505 0.0206 *
## time -0.05792 0.02832 130.00000 -2.045 0.0428 *
## motion 1.37727 1.00953 20.00000 1.364 0.1876
## conditiondrug:time 0.07769 0.04005 130.00000 1.940 0.0546 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.234
## time 0.000 0.000
## motion -0.875 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000RIFG
knitr::include_graphics("RIFG_z7.png")
plot_combination('10', 'RIFG', limits_y = c(-0.15, 0.5))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.7123509 0.7960760 1.4727623
## neurocaps_mcr 0.7123509 NA 0.1962350 0.7743601
## neurocaps_ocr 0.7960760 0.1962350 NA 0.5133078
## tacs 1.4727623 0.7743601 0.5133078 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.86275495 0.95744543 -1.402705
## neurocaps_mcr 0.8627550 NA 0.09483428 -1.808677
## neurocaps_ocr 0.9574454 0.09483428 NA -1.872828
## tacs -1.4027053 -1.80867704 -1.87282849 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.4762475 0.4259879 0.1408151
## neurocaps_mcr 0.4762475 NA 0.8444263 0.4387179
## neurocaps_ocr 0.4259879 0.8444263 NA 0.6077361
## tacs 0.1408151 0.4387179 0.6077361 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.38827221 0.33834248 0.16070473
## neurocaps_mcr 0.3882722 NA 0.92444646 0.07050119
## neurocaps_ocr 0.3383425 0.92444646 NA 0.06109207
## tacs 0.1607047 0.07050119 0.06109207 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.133 -0.28977837 0.5154618 0.133 -0.29662688 0.5177233 0.235 -0.84344104 0.6822367 -0.5 drug
## 2 0.461 0.06586948 0.7326521 0.550 0.17634251 0.7846078 0.709 0.29981490 0.8793056 -1.5 drug
## 3 0.600 0.25478919 0.8106739 0.600 0.24776965 0.8117287 0.750 0.39714005 0.8960820 0.5 drug
## 4 0.140 -0.28298280 0.5208768 0.140 -0.28986075 0.5231210 0.246 -0.81634907 0.6869067 1.5 drug
## 5 0.472 0.08058437 0.7394312 0.472 0.07313599 0.7408262 0.641 0.13630331 0.8511202 drug
## 6 0.282 -0.14080887 0.6210504 0.282 -0.14814511 0.6229417 0.440 -0.34781773 0.7676698 -0.5 neutral
## 7 0.358 -0.05764855 0.6700045 0.358 -0.06511296 0.6717010 0.527 -0.13929590 0.8036138 -1.5 neutral
## 8 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5 neutral
## 9 -0.045 -0.44361712 0.3728581 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 neutral
## 10 0.063 -0.35274973 0.4620252 0.063 -0.35929266 0.4644480 0.119 -1.12155001 0.6342977 neutral
## 11 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.249 -0.17594668 0.5984104 0.374 -0.04657415 0.6817805 0.544 -0.09769852 0.8107842 -1.5DrugvNeutral
## 13 0.608 0.26729981 0.8152265 0.608 0.26032865 0.8162586 0.756 0.41311232 0.8988352 0.5DrugvNeutral
## 14 -0.011 -0.41595961 0.4016943 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5DrugvNeutral
## 15 0.112 -0.30899658 0.4997915 0.134 -0.29592416 0.5182869 0.236 -0.84060310 0.6827259 DrugvNeutralEarly
## 16 0.650 0.33015210 0.8371177 0.650 0.32345970 0.8380384 0.788 0.48880929 0.9118835 DrugvNeutralLate
## 17 0.499 0.11567526 0.7550758 0.499 0.10827656 0.7563987 0.666 0.19539629 0.8613064 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 127.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2004 -0.5378 0.0284 0.5999 3.5567
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01229 0.1108
## Residual 0.06403 0.2530
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.272e-01 3.872e-02 7.469e+01 3.284 0.00156 **
## conditiondrug 1.661e-01 2.219e-02 4.520e+02 7.485 3.76e-13 ***
## time 4.917e-03 1.404e-02 4.520e+02 0.350 0.72625
## motion -5.992e-01 3.186e-01 6.300e+01 -1.880 0.06468 .
## conditiondrug:time -6.029e-04 1.985e-02 4.520e+02 -0.030 0.97578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.287
## time 0.000 0.000
## motion -0.842 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 179.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.3894 0.0024 0.4082 5.8371
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00122 0.03493
## id (Intercept) 0.02297 0.15155
## Residual 0.10432 0.32299
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.18159 0.08263 30.98218 2.198 0.03558 *
## conditiondrug 0.13141 0.04339 28.00000 3.029 0.00523 **
## time 0.01599 0.02682 172.00001 0.596 0.55194
## motion -0.35930 0.72344 27.00000 -0.497 0.62345
## conditiondrug:time -0.03754 0.03793 172.00001 -0.990 0.32374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.263
## time 0.000 0.000
## motion -0.864 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 107
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4826 -0.5020 -0.0037 0.5033 4.3124
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.014729 0.12136
## id (Intercept) 0.004287 0.06548
## Residual 0.084736 0.29110
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.09665 0.07207 27.25103 1.341 0.1909
## conditiondrug 0.11733 0.05714 21.00000 2.053 0.0527 .
## time 0.02199 0.02775 130.00000 0.792 0.4297
## motion 0.11276 0.54399 20.00000 0.207 0.8379
## conditiondrug:time -0.04272 0.03925 130.00000 -1.088 0.2785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.396
## time 0.000 0.000
## motion -0.805 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 111.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5691 -0.4648 -0.0028 0.4447 4.3509
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.01733 0.1317
## id (Intercept) 0.04992 0.2234
## Residual 0.07431 0.2726
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02137 0.13084 22.00018 -0.163 0.872
## conditiondrug 0.07586 0.05714 21.00000 1.328 0.199
## time -0.03981 0.02599 130.00000 -1.532 0.128
## motion 1.22228 1.28850 20.00000 0.949 0.354
## conditiondrug:time 0.05800 0.03676 130.00000 1.578 0.117
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.218
## time 0.000 0.000
## motion -0.879 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000RDLPFC
knitr::include_graphics("RDLPFC_z7.png")
plot_combination('12', 'RDLPFC', limits_y = c(-0.45, 0.1))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.31425131 0.009064596 0.21861556
## neurocaps_mcr 0.314251312 NA -0.212396545 -0.05095833
## neurocaps_ocr 0.009064596 -0.21239655 NA 0.15452834
## tacs 0.218615559 -0.05095833 0.154528341 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.05074138 0.02546767 -0.07062211
## neurocaps_mcr 0.05074138 NA -0.02233374 -0.09538805
## neurocaps_ocr 0.02546767 -0.02233374 NA -0.07761132
## tacs -0.07062211 -0.09538805 -0.07761132 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.7533302 0.9927676 0.8269495
## neurocaps_mcr 0.7533302 NA 0.8317977 0.9593587
## neurocaps_ocr 0.9927676 0.8317977 NA 0.8771932
## tacs 0.8269495 0.9593587 0.8771932 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.9595316 0.9796819 0.9436985
## neurocaps_mcr 0.9595316 NA 0.9821817 0.9240066
## neurocaps_ocr 0.9796819 0.9821817 NA 0.9381372
## tacs 0.9436985 0.9240066 0.9381372 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.037 -0.37551627 0.4411311 0.037 -0.38193421 0.4436122 0.072 -1.23590147 0.6145864 -0.5 drug
## 2 0.153 -0.27069876 0.5305039 0.202 -0.23015741 0.5680815 0.337 -0.59793370 0.7245561 -1.5 drug
## 3 0.071 -0.34593490 0.4681084 0.071 -0.35251375 0.4705138 0.133 -1.08886868 0.6399311 0.5 drug
## 4 0.287 -0.13637989 0.6238166 0.300 -0.12966353 0.6343391 0.461 -0.29796184 0.7762638 1.5 drug
## 5 0.236 -0.18892048 0.5897263 0.236 -0.19613539 0.5917346 0.382 -0.48798116 0.7435092 drug
## 6 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5 neutral
## 7 0.312 -0.10857039 0.6407610 0.357 -0.06553960 0.6714658 0.527 -0.14027262 0.8034454 -1.5 neutral
## 8 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5 neutral
## 9 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 neutral
## 10 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 neutral
## 11 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.508 0.12668409 0.7598382 0.508 0.11930479 0.7611390 0.673 0.21317660 0.8643713 -1.5DrugvNeutral
## 13 0.177 -0.24786607 0.5478653 0.177 -0.25488525 0.5500205 0.301 -0.68415032 0.7096945 0.5DrugvNeutral
## 14 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5DrugvNeutral
## 15 0.044 -0.36929027 0.4469343 0.044 -0.37574316 0.4493995 0.085 -1.20380949 0.6201182 DrugvNeutralEarly
## 16 0.385 -0.02560338 0.6873182 0.385 -0.03308957 0.6889423 0.556 -0.06844391 0.8158269 DrugvNeutralLate
## 17 0.305 -0.11660435 0.6359398 0.305 -0.12398856 0.6377734 0.467 -0.28307521 0.7788298 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 267.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4414 -0.5660 0.0246 0.5511 4.0628
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.002227 0.04719
## id (Intercept) 0.018720 0.13682
## Residual 0.081454 0.28540
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004655 0.047007 73.685426 -0.099 0.9214
## conditiondrug -0.147297 0.026365 63.999993 -5.587 5.08e-07 ***
## time -0.037129 0.015831 387.999987 -2.345 0.0195 *
## motion -0.570243 0.387545 62.999998 -1.471 0.1462
## conditiondrug:time -0.009138 0.022389 387.999987 -0.408 0.6834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.280
## time 0.000 0.000
## motion -0.844 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 264.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3714 -0.3807 0.0382 0.3826 4.7089
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 8.537e-17 9.239e-09
## id (Intercept) 3.319e-02 1.822e-01
## Residual 1.536e-01 3.919e-01
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07436 0.09878 31.05170 -0.753 0.45725
## conditiondrug -0.16547 0.05146 200.00000 -3.215 0.00152 **
## time -0.01313 0.03255 200.00000 -0.403 0.68713
## motion -0.03058 0.86533 27.00000 -0.035 0.97207
## conditiondrug:time -0.01174 0.04603 200.00000 -0.255 0.79902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.260
## time 0.000 0.000
## motion -0.864 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 140.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8340 -0.3946 0.0703 0.3986 4.0753
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.02182 0.1477
## id (Intercept) 0.01711 0.1308
## Residual 0.09622 0.3102
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05875 0.09447 25.22136 -0.622 0.5396
## conditiondrug -0.14793 0.06458 21.00000 -2.291 0.0324 *
## time -0.02566 0.02958 130.00000 -0.868 0.3871
## motion 0.29626 0.72991 20.00000 0.406 0.6891
## conditiondrug:time -0.01035 0.04183 130.00000 -0.247 0.8050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.342
## time 0.000 0.000
## motion -0.824 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 167.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8894 -0.2979 0.0289 0.4702 3.4795
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.008613 0.09281
## id (Intercept) 0.025896 0.16092
## Residual 0.119896 0.34626
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.087081 0.108307 23.218330 -0.804 0.4295
## conditiondrug -0.161470 0.059228 20.999998 -2.726 0.0127 *
## time -0.016560 0.033015 130.000003 -0.502 0.6168
## motion 0.047493 1.051324 20.000002 0.045 0.9644
## conditiondrug:time -0.005481 0.046690 130.000003 -0.117 0.9067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.273
## time 0.000 0.000
## motion -0.866 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000ConditionXTime
VMPFC
knitr::include_graphics("VMPFC_zm11.png")
plot_combination('1', 'VMPFC', limits_y = c(-0.4, 0.5))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.07598867 0.5455601 2.524548
## neurocaps_mcr 0.07598867 NA 0.4319498 2.190228
## neurocaps_ocr 0.54556014 0.43194985 NA 1.565483
## tacs 2.52454835 2.19022832 1.5654825 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -0.7157979 0.4934780 -2.132664
## neurocaps_mcr -0.7157979 NA 0.9771868 -1.340874
## neurocaps_ocr 0.4934780 0.9771868 NA -2.096726
## tacs -2.1326637 -1.3408743 -2.0967259 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.93942811 0.5853683 0.01158471
## neurocaps_mcr 0.93942811 NA 0.6657779 0.02850768
## neurocaps_ocr 0.58536833 0.66577786 NA 0.11746981
## tacs 0.01158471 0.02850768 0.1174698 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.4741162 0.62167491 0.03295233
## neurocaps_mcr 0.47411617 NA 0.32847669 0.17996127
## neurocaps_ocr 0.62167491 0.3284767 NA 0.03601785
## tacs 0.03295233 0.1799613 0.03601785 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.073 -0.34407149 0.4697585 0.073 -0.35066003 0.4721591 0.136 -1.08005068 0.6414511 -0.5 drug
## 2 0.079 -0.33889315 0.4743142 0.223 -0.20974246 0.5824346 0.364 -0.53082051 0.7361247 -1.5 drug
## 3 0.042 -0.37171705 0.4446805 0.042 -0.37815638 0.4471519 0.080 -1.21624271 0.6179750 0.5 drug
## 4 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 drug
## 5 0.114 -0.30707170 0.5013852 0.114 -0.31384216 0.5036910 0.205 -0.91478125 0.6699395 drug
## 6 -0.021 -0.42418129 0.3932883 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5 neutral
## 7 0.334 -0.08416782 0.6550517 0.345 -0.08013093 0.6633308 0.512 -0.17422246 0.7975933 -1.5 neutral
## 8 -0.056 -0.45308572 0.3626098 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5 neutral
## 9 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 neutral
## 10 -0.037 -0.43721342 0.3796780 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 neutral
## 11 -0.001 -0.40777027 0.4099324 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.121 -0.30082855 0.5065169 0.314 -0.11408880 0.6436927 0.478 -0.25756262 0.7832276 -1.5DrugvNeutral
## 13 -0.002 -0.40857976 0.4091240 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5DrugvNeutral
## 14 0.431 0.02938104 0.7152510 0.448 0.04316295 0.7269512 0.619 0.08275399 0.8418897 1.5DrugvNeutral
## 15 -0.048 -0.44653430 0.3697218 0.070 -0.35369437 0.4694629 0.130 -1.09451116 0.6389585 DrugvNeutralEarly
## 16 0.227 -0.19843003 0.5832447 0.227 -0.20561686 0.5852764 0.370 -0.51767681 0.7383904 DrugvNeutralLate
## 17 0.234 -0.19073916 0.5884945 0.311 -0.11713950 0.6418783 0.475 -0.26536355 0.7818829 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 760.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4104 -0.5039 -0.0168 0.5026 5.2248
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.003389 0.05821
## id (Intercept) 0.038847 0.19710
## Residual 0.217516 0.46639
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05693 0.07098 75.08547 0.802 0.4251
## conditiondrug 0.19212 0.04216 64.00001 4.557 2.39e-05 ***
## time 0.05140 0.02587 388.00001 1.987 0.0477 *
## motion -0.99171 0.58216 63.00000 -1.703 0.0934 .
## conditiondrug:time -0.18186 0.03659 388.00001 -4.971 1.00e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.297
## time 0.000 0.000
## motion -0.840 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 315.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0960 -0.5486 0.0200 0.5157 3.4532
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.000841 0.0290
## id (Intercept) 0.013410 0.1158
## Residual 0.205415 0.4532
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04938 0.08809 33.98498 -0.561 0.57873
## conditiondrug 0.18654 0.06000 27.99995 3.109 0.00428 **
## time 0.08372 0.03764 171.99995 2.224 0.02743 *
## motion -0.02502 0.75146 27.00000 -0.033 0.97369
## conditiondrug:time -0.13563 0.05323 171.99995 -2.548 0.01171 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.341
## time 0.000 0.000
## motion -0.842 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 278.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0190 -0.5387 -0.0104 0.6393 3.7770
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01993 0.1412
## Residual 0.25327 0.5033
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.06942 0.10767 26.00213 0.645 0.52474
## conditiondrug 0.14476 0.07587 151.00000 1.908 0.05828 .
## time 0.06562 0.04798 151.00000 1.367 0.17351
## motion -1.40506 0.82846 20.00000 -1.696 0.10540
## conditiondrug:time -0.21991 0.06786 151.00000 -3.241 0.00147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.352
## time 0.000 0.000
## motion -0.821 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 266.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1246 -0.5214 -0.0039 0.5059 4.4048
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.0000 0.0000
## id (Intercept) 0.0325 0.1803
## Residual 0.2307 0.4804
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.078451 0.126663 23.693024 0.619 0.542
## conditiondrug -0.019427 0.072416 151.000000 -0.268 0.789
## time 0.006036 0.045800 151.000000 0.132 0.895
## motion -0.274236 1.224876 20.000000 -0.224 0.825
## conditiondrug:time -0.023216 0.064770 151.000000 -0.358 0.721
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.286
## time 0.000 0.000
## motion -0.863 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000LSTG
knitr::include_graphics("LSFG_z11.png")
plot_combination('3', 'LSTG', limits_y = c(-0.5, 0.3))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 1.9347464 1.0097603 1.2324662
## neurocaps_mcr 1.934746 NA -0.7885670 -0.6726636
## neurocaps_ocr 1.009760 -0.7885670 NA 0.1405627
## tacs 1.232466 -0.6726636 0.1405627 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -0.7002844 0.3893853 -2.225859
## neurocaps_mcr -0.7002844 NA 0.8720940 -1.115811
## neurocaps_ocr 0.3893853 0.8720940 NA -2.073894
## tacs -2.2258591 -1.1158114 -2.0738943 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.05302142 0.3126101 0.2177750
## neurocaps_mcr 0.05302142 NA 0.4303651 0.5011613
## neurocaps_ocr 0.31261014 0.43036512 NA 0.8882154
## tacs 0.21777498 0.50116133 0.8882154 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.4837497 0.69699112 0.02602362
## neurocaps_mcr 0.48374969 NA 0.38315709 0.26450288
## neurocaps_ocr 0.69699112 0.3831571 NA 0.03808913
## tacs 0.02602362 0.2645029 0.03808913 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5 drug
## 2 0.122 -0.3002244 0.5070105 0.146 -0.2846024 0.5272714 0.254 -0.7956483 0.6904750 -1.5 drug
## 3 0.215 -0.2106148 0.5747915 0.219 -0.2138283 0.5796010 0.359 -0.5439736 0.7338575 0.5 drug
## 4 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 1.5 drug
## 5 0.078 -0.3394387 0.4738363 0.078 -0.3460512 0.4762250 0.145 -1.0583433 0.6451929 drug
## 6 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5 neutral
## 7 0.135 -0.2874377 0.5173342 0.217 -0.2156613 0.5783235 0.357 -0.5499188 0.7328326 -1.5 neutral
## 8 -0.002 -0.4084223 0.4092813 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 0.5 neutral
## 9 0.038 -0.3747622 0.4418376 0.055 -0.3664769 0.4579340 0.104 -1.1569490 0.6281958 1.5 neutral
## 10 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 neutral
## 11 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5DrugvNeutral
## 12 -0.009 -0.4148260 0.4028425 0.126 -0.3030504 0.5125382 0.224 -0.8696480 0.6777193 -1.5DrugvNeutral
## 13 0.113 -0.3082963 0.5003719 0.113 -0.3150611 0.5026808 0.203 -0.9199683 0.6690453 0.5DrugvNeutral
## 14 -0.003 -0.4096944 0.4080088 0.007 -0.4070884 0.4193681 0.015 -1.3731840 0.5909223 1.5DrugvNeutral
## 15 -0.009 -0.4142570 0.4034180 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 DrugvNeutralEarly
## 16 0.110 -0.3107377 0.4983452 0.110 -0.3174910 0.5006603 0.199 -0.9303644 0.6672533 DrugvNeutralLate
## 17 0.282 -0.1409204 0.6209804 0.282 -0.1482564 0.6228720 0.440 -0.3481246 0.7676169 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 488
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7012 -0.5367 0.0335 0.5085 4.7125
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.0000 0.0000
## id (Intercept) 0.0166 0.1288
## Residual 0.1326 0.3642
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.008367 0.050056 77.948844 -0.167 0.8677
## conditiondrug -0.043988 0.031941 452.000000 -1.377 0.1691
## time 0.100070 0.020201 452.000000 4.954 1.03e-06 ***
## motion -0.801334 0.407467 63.000000 -1.967 0.0536 .
## conditiondrug:time -0.152424 0.028569 452.000000 -5.335 1.51e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.319
## time 0.000 0.000
## motion -0.833 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 352.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4520 -0.4321 -0.0093 0.3784 8.6760
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 9.759e-16 3.124e-08
## id (Intercept) 2.711e-02 1.646e-01
## Residual 2.358e-01 4.856e-01
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.16974 0.10412 32.82824 -1.630 0.11262
## conditiondrug -0.18197 0.06376 200.00000 -2.854 0.00477 **
## time 0.07866 0.04033 200.00000 1.951 0.05250 .
## motion 1.86176 0.89932 27.00000 2.070 0.04812 *
## conditiondrug:time -0.10776 0.05703 200.00000 -1.889 0.06028 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.306
## time 0.000 0.000
## motion -0.852 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 204.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7379 -0.4146 0.0566 0.5143 3.8043
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.02198 0.1482
## Residual 0.15951 0.3994
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.007111 0.095683 24.596763 0.074 0.941362
## conditiondrug -0.112810 0.060209 151.000000 -1.874 0.062913 .
## time 0.131431 0.038080 151.000000 3.451 0.000723 ***
## motion -0.286169 0.746753 20.000000 -0.383 0.705601
## conditiondrug:time -0.176162 0.053853 151.000000 -3.271 0.001327 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.315
## time 0.000 0.000
## motion -0.832 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 201.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2358 -0.4387 0.0239 0.4450 3.5822
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.07051 0.2655
## Residual 0.14269 0.3777
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03620 0.14842 21.55345 -0.244 0.8096
## conditiondrug -0.12446 0.05695 151.00000 -2.186 0.0304 *
## time 0.01476 0.03602 151.00000 0.410 0.6825
## motion 0.31504 1.46995 20.00000 0.214 0.8325
## conditiondrug:time -0.02244 0.05093 151.00000 -0.440 0.6602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.192
## time 0.000 0.000
## motion -0.884 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000RSTG
knitr::include_graphics("RSFG_z7.png")
plot_combination('4', 'RSTG', limits_y = c(-0.4, 0.25))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 2.2100759 0.3935193 1.6060658
## neurocaps_mcr 2.2100759 NA -1.5704733 -0.2390023
## neurocaps_ocr 0.3935193 -1.5704733 NA 1.1447217
## tacs 1.6060658 -0.2390023 1.1447217 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -1.3968480 -0.6932403 -2.4421241
## neurocaps_mcr -1.3968480 NA 0.5854781 -0.9980761
## neurocaps_ocr -0.6932403 0.5854781 NA -1.5448472
## tacs -2.4421241 -0.9980761 -1.5448472 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.0270999 0.6939360 0.1082595
## neurocaps_mcr 0.0270999 NA 0.1163050 0.8111038
## neurocaps_ocr 0.6939360 0.1163050 NA 0.2523245
## tacs 0.1082595 0.8111038 0.2523245 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.1624593 0.4881588 0.01460113
## neurocaps_mcr 0.16245930 NA 0.5582263 0.31824245
## neurocaps_ocr 0.48815879 0.5582263 NA 0.12238323
## tacs 0.01460113 0.3182424 0.1223832 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5 drug
## 2 0.395 -0.01412295 0.6933300 0.395 -0.02161320 0.6949285 0.566 -0.04418130 0.8200092 -1.5 drug
## 3 0.386 -0.02518190 0.6875407 0.386 -0.03266827 0.6891638 0.557 -0.06754306 0.8159822 0.5 drug
## 4 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 drug
## 5 0.165 -0.25966198 0.5389808 0.165 -0.26663577 0.5411658 0.283 -0.72715781 0.7022811 drug
## 6 0.067 -0.34948960 0.4649449 0.067 -0.35604980 0.4673594 0.126 -1.10583024 0.6370074 -0.5 neutral
## 7 0.070 -0.34684083 0.4673042 0.090 -0.33541924 0.4854722 0.166 -1.00941603 0.6536268 -1.5 neutral
## 8 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5 neutral
## 9 0.034 -0.37789252 0.4388980 0.036 -0.38257773 0.4430066 0.070 -1.23927418 0.6140050 1.5 neutral
## 10 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 neutral
## 11 -0.012 -0.41688235 0.4007577 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.116 -0.30546157 0.5027142 0.166 -0.26578608 0.5418121 0.284 -0.72400175 0.7028251 -1.5DrugvNeutral
## 13 0.193 -0.23183343 0.5596598 0.193 -0.23891098 0.5617747 0.324 -0.62781349 0.7194056 0.5DrugvNeutral
## 14 0.050 -0.36450590 0.4513477 0.050 -0.37098524 0.4538006 0.095 -1.17957565 0.6242955 1.5DrugvNeutral
## 15 -0.003 -0.40969686 0.4080063 0.050 -0.37129867 0.4535119 0.094 -1.18116075 0.6240223 DrugvNeutralEarly
## 16 0.167 -0.25744436 0.5406647 0.167 -0.26442684 0.5428441 0.286 -0.71896816 0.7036928 DrugvNeutralLate
## 17 0.283 -0.13993787 0.6215959 0.304 -0.12516779 0.6370622 0.466 -0.28615267 0.7782994 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 460.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2027 -0.4741 0.0263 0.5178 4.4562
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01976 0.1406
## Residual 0.12372 0.3517
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.006044 0.051259 76.064818 0.118 0.906
## conditiondrug -0.037236 0.030849 451.999997 -1.207 0.228
## time 0.094633 0.019511 451.999997 4.850 1.70e-06 ***
## motion -0.530157 0.419862 63.000004 -1.263 0.211
## conditiondrug:time -0.152584 0.027593 451.999997 -5.530 5.43e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.301
## time 0.000 0.000
## motion -0.839 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 238.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5813 -0.4029 0.0069 0.4913 5.5167
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.006746 0.08213
## id (Intercept) 0.028415 0.16857
## Residual 0.132720 0.36431
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13853 0.09533 31.40335 -1.453 0.15610
## conditiondrug -0.17176 0.05247 28.00002 -3.273 0.00283 **
## time 0.05542 0.03025 172.00005 1.832 0.06869 .
## motion 1.17457 0.83155 27.00000 1.413 0.16923
## conditiondrug:time -0.08147 0.04279 172.00005 -1.904 0.05857 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.275
## time 0.000 0.000
## motion -0.861 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 129.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8687 -0.4822 0.0144 0.6112 4.2070
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01532 0.1238
## Residual 0.10256 0.3202
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.06867 0.07824 24.39391 0.878 0.38869
## conditiondrug -0.05978 0.04828 151.00000 -1.238 0.21754
## time 0.09106 0.03053 151.00000 2.982 0.00334 **
## motion -0.69649 0.61190 20.00000 -1.138 0.26847
## conditiondrug:time -0.11706 0.04318 151.00000 -2.711 0.00749 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.309
## time 0.000 0.000
## motion -0.834 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 187.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1370 -0.4503 0.0529 0.4722 4.2635
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.01385 0.1177
## id (Intercept) 0.04863 0.2205
## Residual 0.12704 0.3564
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.030549 0.134884 22.411828 -0.226 0.8229
## conditiondrug -0.151911 0.064393 20.999999 -2.359 0.0281 *
## time -0.007535 0.033984 129.999999 -0.222 0.8249
## motion 0.688983 1.321838 20.000001 0.521 0.6079
## conditiondrug:time -0.017246 0.048061 129.999999 -0.359 0.7203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.239
## time 0.000 0.000
## motion -0.874 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000LVStriatum
knitr::include_graphics("LVStri_zm1.png")
plot_combination('5', 'LVSTRI', limits_y = c(-0.22, 0.35))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.7779924 0.1985035 2.713589
## neurocaps_mcr 0.7779924 NA -0.3694164 1.641814
## neurocaps_ocr 0.1985035 -0.3694164 NA 1.762054
## tacs 2.7135890 1.6418139 1.7620536 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.1731414 0.9808995 -2.384295
## neurocaps_mcr 0.1731414 NA 0.7411386 -2.132148
## neurocaps_ocr 0.9808995 0.7411386 NA -2.544832
## tacs -2.3842952 -2.1321485 -2.5448317 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.4365735 0.84265117 0.006655868
## neurocaps_mcr 0.436573519 NA 0.71181736 0.100628576
## neurocaps_ocr 0.842651170 0.7118174 NA 0.078060239
## tacs 0.006655868 0.1006286 0.07806024 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.86254031 0.32664230 0.01711187
## neurocaps_mcr 0.86254031 NA 0.45860940 0.03299464
## neurocaps_ocr 0.32664230 0.45860940 NA 0.01093304
## tacs 0.01711187 0.03299464 0.01093304 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 -0.011 -0.4163157 0.4013330 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5 drug
## 2 -0.233 -0.5842313 0.1969920 0.048 -0.37265010 0.4522652 0.092 -1.18801360 0.6228410 -1.5 drug
## 3 0.180 -0.2452362 0.5498219 0.180 -0.25226520 0.5519705 0.305 -0.67474510 0.7113157 0.5 drug
## 4 0.000 -0.4070147 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 drug
## 5 -0.042 -0.4417737 0.3748305 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 drug
## 6 0.281 -0.1420422 0.6202767 0.281 -0.14937578 0.6221709 0.439 -0.35121451 0.7670843 -0.5 neutral
## 7 0.128 -0.2940845 0.5119968 0.129 -0.30031985 0.5147496 0.228 -0.85844898 0.6796497 -1.5 neutral
## 8 0.000 -0.4070147 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 0.5 neutral
## 9 0.000 -0.4070147 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 1.5 neutral
## 10 0.000 -0.4070147 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 neutral
## 11 0.000 -0.4070147 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.40858948 0.5848192 -0.5DrugvNeutral
## 12 0.050 -0.3645727 0.4512863 0.440 0.03306215 0.7221461 0.611 0.06400805 0.8386583 -1.5DrugvNeutral
## 13 0.165 -0.2592465 0.5392968 0.165 -0.26622191 0.5414807 0.284 -0.72561967 0.7025462 0.5DrugvNeutral
## 14 0.202 -0.2236710 0.5655438 0.202 -0.23077683 0.5676383 0.336 -0.60002570 0.7241955 1.5DrugvNeutral
## 15 -0.131 -0.5104297 0.2960213 0.125 -0.30351702 0.5121592 0.223 -0.87157053 0.6773879 DrugvNeutralEarly
## 16 0.236 -0.1889639 0.5896970 0.236 -0.19617868 0.5917054 0.382 -0.48811515 0.7434861 DrugvNeutralLate
## 17 0.218 -0.2078345 0.5767352 0.317 -0.11044641 0.6458480 0.482 -0.24831874 0.7848210 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 153.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5172 -0.5441 -0.0123 0.5962 4.8206
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.000000 0.00000
## id (Intercept) 0.008348 0.09137
## Residual 0.069458 0.26355
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.09502 0.03590 78.26601 2.647 0.009815 **
## conditiondrug 0.11960 0.02311 452.00000 5.174 3.45e-07 ***
## time 0.02737 0.01462 452.00000 1.872 0.061827 .
## motion -1.21634 0.29192 63.00000 -4.167 9.59e-05 ***
## conditiondrug:time -0.07181 0.02067 452.00000 -3.473 0.000564 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.322
## time 0.000 0.000
## motion -0.832 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 137.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7549 -0.4475 0.0024 0.4298 6.3774
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.02711 0.1647
## Residual 0.08492 0.2914
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13960 0.08317 30.08911 -1.679 0.1036
## conditiondrug 0.08482 0.03826 200.00000 2.217 0.0278 *
## time 0.05273 0.02420 200.00000 2.179 0.0305 *
## motion 0.95340 0.73434 27.00000 1.298 0.2052
## conditiondrug:time -0.07873 0.03422 200.00000 -2.300 0.0225 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.230
## time 0.000 0.000
## motion -0.871 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 154.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4172 -0.5649 0.0638 0.5921 3.0391
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 9.202e-19 9.592e-10
## id (Intercept) 2.537e-02 1.593e-01
## Residual 1.155e-01 3.398e-01
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05760 0.09215 23.46867 0.625 0.53792
## conditiondrug 0.10844 0.05123 151.00000 2.117 0.03592 *
## time 0.03162 0.03240 151.00000 0.976 0.33072
## motion -0.72282 0.72777 20.00000 -0.993 0.33248
## conditiondrug:time -0.12112 0.04582 151.00000 -2.643 0.00908 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.278
## time 0.000 0.000
## motion -0.842 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 73.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3930 -0.5325 0.0113 0.5596 2.3392
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01882 0.1372
## Residual 0.07131 0.2670
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.063214 0.084064 22.495706 -0.752 0.460
## conditiondrug -0.006365 0.040257 150.999999 -0.158 0.875
## time -0.011709 0.025461 150.999999 -0.460 0.646
## motion 0.540027 0.823652 20.000001 0.656 0.520
## conditiondrug:time 0.027189 0.036007 150.999999 0.755 0.451
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.239
## time 0.000 0.000
## motion -0.874 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000RVStriatum
knitr::include_graphics("RVStri_zm1.png")
plot_combination('6', 'RVSTRI', limits_y = c(-0.2, 0.3))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.5332700 0.7414731 1.9582658
## neurocaps_mcr 0.5332700 NA 0.2394436 1.2620420
## neurocaps_ocr 0.7414731 0.2394436 NA 0.9295674
## tacs 1.9582658 1.2620420 0.9295674 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -0.7847964 0.7936411 -2.304104
## neurocaps_mcr -0.7847964 NA 1.2817258 -1.361958
## neurocaps_ocr 0.7936411 1.2817258 NA -2.460801
## tacs -2.3041039 -1.3619583 -2.4608006 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.5938467 0.4584066 0.05019883
## neurocaps_mcr 0.59384666 NA 0.8107616 0.20693369
## neurocaps_ocr 0.45840665 0.8107616 NA 0.35259512
## tacs 0.05019883 0.2069337 0.3525951 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.4325729 0.42740442 0.02121681
## neurocaps_mcr 0.43257295 NA 0.19993887 0.17321106
## neurocaps_ocr 0.42740442 0.1999389 NA 0.01386274
## tacs 0.02121681 0.1732111 0.01386274 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 -0.013 -0.41814081 0.3994775 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 -0.5 drug
## 2 -0.183 -0.54883570 0.2465632 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 -1.5 drug
## 3 0.173 -0.25189290 0.5448524 0.173 -0.25889682 0.5470178 0.295 -0.6986795 0.7071900 0.5 drug
## 4 0.209 -0.21594380 0.5710411 0.209 -0.22307541 0.5731163 0.346 -0.5742524 0.7286381 1.5 drug
## 5 -0.004 -0.41043615 0.4072652 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 drug
## 6 0.466 0.07263964 0.7357876 0.466 0.06518258 0.7371991 0.636 0.1223876 0.8487215 -0.5 neutral
## 7 0.183 -0.24181183 0.5523566 0.183 -0.24885350 0.5544966 0.310 -0.6625964 0.7134098 -1.5 neutral
## 8 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 0.5 neutral
## 9 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 1.5 neutral
## 10 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 neutral
## 11 -0.007 -0.41276396 0.4049248 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 -0.5DrugvNeutral
## 12 -0.103 -0.48941402 0.3213702 0.030 -0.38776739 0.4380956 0.059 -1.2667322 0.6092719 -1.5DrugvNeutral
## 13 0.204 -0.22138222 0.5671794 0.204 -0.22849574 0.5692682 0.339 -0.5923383 0.7255206 0.5DrugvNeutral
## 14 0.081 -0.33694092 0.4760205 0.093 -0.33326886 0.4873205 0.170 -0.9997099 0.6552999 1.5DrugvNeutral
## 15 -0.089 -0.47856170 0.3340204 0.046 -0.37437359 0.4506705 0.088 -1.1967960 0.6213272 DrugvNeutralEarly
## 16 0.149 -0.27471736 0.5273771 0.149 -0.28163020 0.5296003 0.259 -0.7840814 0.6924689 DrugvNeutralLate
## 17 0.308 -0.11316548 0.6380107 0.319 -0.10837702 0.6470672 0.484 -0.2431006 0.7857205 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 80.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9640 -0.4675 0.0041 0.5317 5.0395
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.00000
## id (Intercept) 0.00823 0.09072
## Residual 0.05978 0.24450
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05576 0.03435 77.20889 1.623 0.108602
## conditiondrug 0.10634 0.02144 452.00000 4.959 1e-06 ***
## time 0.01734 0.01356 452.00000 1.279 0.201721
## motion -0.99538 0.28031 63.00000 -3.551 0.000733 ***
## conditiondrug:time -0.06362 0.01918 452.00000 -3.317 0.000984 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.312
## time 0.000 0.000
## motion -0.835 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 75.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7190 -0.5456 -0.0613 0.5309 5.0944
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01216 0.1103
## Residual 0.06758 0.2600
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14029 0.06220 31.54753 -2.255 0.0312 *
## conditiondrug 0.08485 0.03413 200.00000 2.486 0.0137 *
## time 0.01649 0.02159 200.00000 0.764 0.4458
## motion 0.61150 0.54268 27.00000 1.127 0.2697
## conditiondrug:time -0.03532 0.03053 200.00000 -1.157 0.2487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.274
## time 0.000 0.000
## motion -0.861 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 76.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3723 -0.5367 0.0829 0.5963 2.4074
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 1.243e-15 3.526e-08
## id (Intercept) 1.485e-02 1.218e-01
## Residual 7.369e-02 2.715e-01
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03235 0.07179 23.66917 0.451 0.65636
## conditiondrug 0.07209 0.04092 151.00000 1.762 0.08017 .
## time 0.00827 0.02588 151.00000 0.320 0.74977
## motion -0.56711 0.56573 20.00000 -1.002 0.32811
## conditiondrug:time -0.09641 0.03660 151.00000 -2.634 0.00932 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.285
## time 0.000 0.000
## motion -0.841 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 61
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3285 -0.4517 0.0488 0.5717 2.5342
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.00000 0.0000
## id (Intercept) 0.01882 0.1372
## Residual 0.06581 0.2565
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15060 0.08288 22.35899 -1.817 0.0826 .
## conditiondrug 0.01975 0.03867 151.00000 0.511 0.6104
## time -0.02562 0.02446 151.00000 -1.047 0.2966
## motion 1.20248 0.81333 20.00000 1.478 0.1549
## conditiondrug:time 0.02752 0.03459 151.00000 0.796 0.4276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.233
## time 0.000 0.000
## motion -0.876 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000RAmy
knitr::include_graphics("RAmy_zm16.png")
plot_combination('8', 'RAmy', limits_y = c(-0.05, 0.75))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.7234911 2.4657736 2.7482396
## neurocaps_mcr 0.7234911 NA 1.3756300 1.8963280
## neurocaps_ocr 2.4657736 1.3756300 NA 0.7971716
## tacs 2.7482396 1.8963280 0.7971716 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -0.6276469 0.3958587 -1.4661307
## neurocaps_mcr -0.6276469 NA 0.8888891 -0.8061678
## neurocaps_ocr 0.3958587 0.8888891 NA -1.6096743
## tacs -1.4661307 -0.8061678 -1.6096743 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.46937821 0.01367177 0.005991622
## neurocaps_mcr 0.469378211 NA 0.16893622 0.057916681
## neurocaps_ocr 0.013671771 0.16893622 NA 0.425351385
## tacs 0.005991622 0.05791668 0.42535139 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.5302353 0.6922093 0.1426127
## neurocaps_mcr 0.5302353 NA 0.3740627 0.4201461
## neurocaps_ocr 0.6922093 0.3740627 NA 0.1074690
## tacs 0.1426127 0.4201461 0.1074690 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5 drug
## 2 -0.028 -0.4299547 0.3873026 0.001 -0.4123668 0.4141265 0.002 -1.4034836 0.5856994 -1.5 drug
## 3 0.080 -0.3379906 0.4751038 0.081 -0.3436572 0.4783232 0.150 -1.0471881 0.6471158 0.5 drug
## 4 0.029 -0.3828382 0.4342177 0.029 -0.3892143 0.4367175 0.056 -1.2744711 0.6079379 1.5 drug
## 5 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 drug
## 6 -0.169 -0.5392473 0.2593116 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5 neutral
## 7 0.213 -0.2123710 0.5735592 0.213 -0.2195142 0.5756255 0.351 -0.5625066 0.7306628 -1.5 neutral
## 8 0.221 -0.2048928 0.5787821 0.227 -0.2057729 0.5851693 0.370 -0.5181713 0.7383051 0.5 neutral
## 9 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 1.5 neutral
## 10 -0.023 -0.4256577 0.3917642 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 neutral
## 11 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 -0.5DrugvNeutral
## 12 0.169 -0.2555671 0.5420853 0.214 -0.2181521 0.5765813 0.353 -0.5580423 0.7314324 -1.5DrugvNeutral
## 13 0.295 -0.1275845 0.6292541 0.295 -0.1349480 0.6311138 0.455 -0.3119998 0.7738440 0.5DrugvNeutral
## 14 0.000 -0.4070147 0.4106858 0.000 -0.4132470 0.4132470 0.000 -1.4085895 0.5848192 1.5DrugvNeutral
## 15 0.244 -0.1812008 0.5949153 0.324 -0.1030006 0.6502167 0.489 -0.2296560 0.7880380 DrugvNeutralEarly
## 16 0.137 -0.2863589 0.5181947 0.137 -0.2932223 0.5204475 0.240 -0.8297439 0.6845978 DrugvNeutralLate
## 17 0.144 -0.2793620 0.5237359 0.144 -0.2862554 0.5259709 0.252 -0.8021227 0.6893590 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 596.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3756 -0.5396 0.0180 0.5297 5.3356
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.0007294 0.02701
## id (Intercept) 0.0467436 0.21620
## Residual 0.1527508 0.39083
## Number of obs: 520, groups: condition:id, 130; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.223221 0.069208 71.367835 3.225 0.001900 **
## conditiondrug 0.292493 0.034604 63.999880 8.453 5.15e-12 ***
## time -0.004978 0.021680 387.999871 -0.230 0.818492
## motion -0.568463 0.575556 63.000002 -0.988 0.327090
## conditiondrug:time -0.105186 0.030659 387.999871 -3.431 0.000667 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.250
## time 0.000 0.000
## motion -0.851 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 219.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6537 -0.6263 -0.0258 0.5302 3.8533
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.01345 0.1160
## id (Intercept) 0.01921 0.1386
## Residual 0.11989 0.3462
## Number of obs: 232, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.16587 0.08862 32.64059 1.872 0.070230 .
## conditiondrug 0.24565 0.05472 28.00000 4.489 0.000112 ***
## time 0.04765 0.02875 172.00000 1.657 0.099304 .
## motion 0.02155 0.76478 27.00000 0.028 0.977723
## conditiondrug:time -0.07322 0.04066 172.00000 -1.801 0.073518 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.309
## time 0.000 0.000
## motion -0.851 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 140.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3272 -0.5281 0.0024 0.5381 3.5504
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 3.143e-16 1.773e-08
## id (Intercept) 2.639e-02 1.625e-01
## Residual 1.056e-01 3.249e-01
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.27838 0.09160 23.17985 3.039 0.00580 **
## conditiondrug 0.14462 0.04898 151.00000 2.953 0.00366 **
## time 0.01792 0.03098 151.00000 0.578 0.56386
## motion -1.16955 0.72570 20.00000 -1.612 0.12271
## conditiondrug:time -0.12635 0.04381 151.00000 -2.884 0.00450 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.267
## time 0.000 0.000
## motion -0.845 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + motion + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 177.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89617 -0.49440 -0.00314 0.54474 2.35083
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.02513 0.1585
## id (Intercept) 0.02384 0.1544
## Residual 0.11978 0.3461
## Number of obs: 176, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.24934 0.11658 24.02819 2.139 0.0428 *
## conditiondrug 0.07601 0.07076 21.00000 1.074 0.2949
## time -0.04487 0.03300 130.00000 -1.360 0.1762
## motion -0.10212 1.12100 20.00000 -0.091 0.9283
## conditiondrug:time -0.02332 0.04667 130.00000 -0.500 0.6181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time motion
## conditindrg -0.303
## time 0.000 0.000
## motion -0.858 0.000 0.000
## cndtndrg:tm 0.000 0.000 -0.707 0.000Self-Report and RTs
Craving
plot_combination('craving', 'Craving', limits_y = c(1, 4), imaging = FALSE, forest_range = c(-0.5, 2.5))## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.1576549 -3.5832372 -3.6285460
## neurocaps_mcr 0.1576549 NA -2.8131019 -2.8858501
## neurocaps_ocr -3.5832372 -2.8131019 NA -0.1371176
## tacs -3.6285460 -2.8858501 -0.1371176 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -1.36784194 -0.6971554 -1.10796210
## neurocaps_mcr -1.3678419 NA 0.4683281 0.08924022
## neurocaps_ocr -0.6971554 0.46832809 NA -0.34567535
## tacs -1.1079621 0.08924022 -0.3456754 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.87472870 0.0003393621 0.000285022
## neurocaps_mcr 0.8747287022 NA 0.0049066102 0.003903580
## neurocaps_ocr 0.0003393621 0.00490661 NA 0.890937842
## tacs 0.0002850220 0.00390358 0.8909378416 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.1713616 0.4857055 0.2678782
## neurocaps_mcr 0.1713616 NA 0.6395500 0.9288910
## neurocaps_ocr 0.4857055 0.6395500 NA 0.7295867
## tacs 0.2678782 0.9288910 0.7295867 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.640 0.31473935 0.8318957 0.648 0.321032941 0.8372305 0.787 0.48603321 0.9114049 -0.5 drug
## 2 0.210 -0.21495602 0.5717387 0.210 -0.222090866 0.5738115 0.348 -0.57099437 0.7291998 -1.5 drug
## 3 0.290 -0.13220264 0.6264082 0.290 -0.139556940 0.6282790 0.450 -0.32438391 0.7717092 0.5 drug
## 4 0.408 0.00172231 0.7014687 0.408 -0.005770302 0.7030322 0.580 -0.01160758 0.8256241 1.5 drug
## 5 0.524 0.14929176 0.7694099 0.524 0.141958008 0.7706655 0.688 0.24862212 0.8704812 drug
## 6 0.593 0.24556160 0.8072723 0.601 0.250377263 0.8126749 0.751 0.40048275 0.8966582 -0.5 neutral
## 7 0.457 0.06109960 0.7304258 0.478 0.081211991 0.7444697 0.647 0.15022399 0.8535198 -1.5 neutral
## 8 0.256 -0.16844405 0.6033506 0.256 -0.175714827 0.6053087 0.408 -0.42634475 0.7541337 0.5 neutral
## 9 0.227 -0.19834601 0.5833024 0.227 -0.205533104 0.5853339 0.370 -0.51741137 0.7384361 1.5 neutral
## 10 0.471 0.07915571 0.7387789 0.471 0.071705701 0.7401768 0.640 0.13381603 0.8506915 neutral
## 11 0.549 0.18239307 0.7829393 0.599 0.246868541 0.8114011 0.749 0.39598167 0.8958823 -0.5DrugvNeutral
## 12 0.169 -0.25556597 0.5420861 0.169 -0.262555756 0.5442607 0.289 -0.71206944 0.7048819 -1.5DrugvNeutral
## 13 0.450 0.05319355 0.7267043 0.456 0.052062876 0.7311302 0.626 0.09897294 0.8446854 0.5DrugvNeutral
## 14 0.585 0.23387444 0.8029099 0.585 0.226779286 0.8040033 0.738 0.36971489 0.8913546 1.5DrugvNeutral
## 15 0.470 0.07798528 0.7382435 0.470 0.070533954 0.7396438 0.640 0.13177341 0.8503394 DrugvNeutralEarly
## 16 0.747 0.48816843 0.8858083 0.753 0.493637658 0.8895781 0.859 0.66098716 0.9415626 DrugvNeutralLate
## 17 0.698 0.40666757 0.8617427 0.698 0.400395025 0.8625346 0.822 0.57183154 0.9261944 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 1014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.06640 -0.53291 0.05142 0.55519 3.06609
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.1242 0.3524
## id (Intercept) 0.3488 0.5906
## Residual 0.2824 0.5314
## Number of obs: 494, groups: condition:id, 129; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.69379 0.09207 91.37980 18.40 < 2e-16 ***
## conditiondrug 1.31929 0.07862 63.39930 16.78 < 2e-16 ***
## time 0.18236 0.03106 368.81187 5.87 9.7e-09 ***
## conditiondrug:time -0.10600 0.04327 367.10038 -2.45 0.0148 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.427
## time -0.013 0.013
## cndtndrg:tm 0.009 -0.015 -0.718
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 370.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3459 -0.4088 -0.0669 0.1513 3.7800
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.36796 0.6066
## id (Intercept) 0.07349 0.2711
## Residual 0.14943 0.3866
## Number of obs: 228, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.08915 0.12866 54.96792 8.465 1.54e-11 ***
## conditiondrug 1.29014 0.16737 27.95315 7.708 2.16e-08 ***
## time 0.04062 0.03221 168.23056 1.261 0.209
## conditiondrug:time -0.01991 0.04571 168.28819 -0.436 0.664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.651
## time -0.001 0.001
## cndtndrg:tm 0.001 -0.003 -0.705
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 296.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8107 -0.5292 0.0609 0.4771 2.8296
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.1905 0.4365
## id (Intercept) 0.1031 0.3212
## Residual 0.1874 0.4329
## Number of obs: 174, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.38243 0.12454 38.34890 11.101 1.53e-13 ***
## conditiondrug 1.91698 0.14711 21.01962 13.031 1.55e-11 ***
## time 0.14109 0.04182 128.19406 3.374 0.000981 ***
## conditiondrug:time -0.05492 0.05914 128.18996 -0.929 0.354867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.591
## time -0.007 0.006
## cndtndrg:tm 0.005 -0.009 -0.707
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 291.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64887 -0.62209 0.04536 0.61595 2.45138
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.2149 0.4636
## id (Intercept) 0.1002 0.3165
## Residual 0.1777 0.4215
## Number of obs: 173, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.38609 0.12795 39.04699 10.833 2.49e-13 ***
## conditiondrug 1.94616 0.15383 20.95862 12.651 2.81e-11 ***
## time 0.17284 0.04025 127.16548 4.295 3.44e-05 ***
## conditiondrug:time -0.02645 0.05730 127.31114 -0.462 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.600
## time -0.002 0.002
## cndtndrg:tm 0.002 -0.004 -0.702Craving RT
plot_combination('craving_rt', 'CravingRT', limits_y = c(1, 3.5), imaging = FALSE)## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -2.385365 1.594898 -0.8158398
## neurocaps_mcr -2.3853647 NA 3.496007 1.4708419
## neurocaps_ocr 1.5948985 3.496007 NA -2.1848318
## tacs -0.8158398 1.470842 -2.184832 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.09861327 -0.1295825 0.3083629
## neurocaps_mcr 0.09861327 NA -0.1923227 0.1838783
## neurocaps_ocr -0.12958247 -0.19232270 NA 0.3477431
## tacs 0.30836289 0.18387829 0.3477431 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.0170621969 0.1107349440 0.41459174
## neurocaps_mcr 0.0170622 NA 0.0004722765 0.14133389
## neurocaps_ocr 0.1107349 0.0004722765 NA 0.02890118
## tacs 0.4145917 0.1413338910 0.0289011789 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.9214453 0.8968968 0.7578062
## neurocaps_mcr 0.9214453 NA 0.8474894 0.8541089
## neurocaps_ocr 0.8968968 0.8474894 NA 0.7280331
## tacs 0.7578062 0.8541089 0.7280331 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.010 -0.39852452 0.4190756 0.010 -0.404808316 0.4216154 0.020 -1.36026200 0.5931497 -0.5 drug
## 2 -0.037 -0.43731361 0.3795720 0.000 -0.413247031 0.4132470 0.000 -1.40858948 0.5848192 -1.5 drug
## 3 0.000 -0.40701472 0.4106858 0.000 -0.413247031 0.4132470 0.000 -1.40858948 0.5848192 0.5 drug
## 4 0.439 0.03843318 0.7196487 0.439 0.030949557 0.7211326 0.610 0.06004088 0.8379745 1.5 drug
## 5 0.036 -0.37652310 0.4401861 0.036 -0.382935331 0.4426698 0.070 -1.24115137 0.6136814 drug
## 6 -0.012 -0.41685610 0.4007843 0.036 -0.383271168 0.4423533 0.069 -1.24291633 0.6133772 -0.5 neutral
## 7 -0.140 -0.51762725 0.2870705 0.064 -0.358693986 0.4649868 0.120 -1.11863596 0.6348000 -1.5 neutral
## 8 0.471 0.07975234 0.7390515 0.471 0.072303002 0.7404481 0.641 0.13485554 0.8508707 0.5 neutral
## 9 0.325 -0.09494472 0.6488050 0.359 -0.063215014 0.6727456 0.529 -0.13496163 0.8043609 1.5 neutral
## 10 0.355 -0.06111637 0.6680822 0.564 0.196853469 0.7926416 0.721 0.32895166 0.8843280 neutral
## 11 0.021 -0.38937000 0.4279682 0.033 -0.385178854 0.4405514 0.065 -1.25297855 0.6116427 -0.5DrugvNeutral
## 12 -0.012 -0.41669523 0.4009477 0.000 -0.413247031 0.4132470 0.000 -1.40858948 0.5848192 -1.5DrugvNeutral
## 13 0.188 -0.23692188 0.5559509 0.188 -0.243981312 0.5580785 0.317 -0.64543725 0.7163677 0.5DrugvNeutral
## 14 0.372 -0.04139961 0.6788837 0.420 0.007584464 0.7097232 0.591 0.01505475 0.8302200 1.5DrugvNeutral
## 15 -0.055 -0.45212917 0.3636542 0.000 -0.413247031 0.4132470 0.000 -1.40858948 0.5848192 DrugvNeutralEarly
## 16 0.116 -0.30521874 0.5029143 0.125 -0.303592532 0.5120978 0.223 -0.87188189 0.6773342 DrugvNeutralLate
## 17 -0.054 -0.45070979 0.3652001 0.000 -0.413247031 0.4132470 0.000 -1.40858948 0.5848192 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 1320.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1302 -0.6149 -0.2205 0.4779 3.4277
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.1413 0.3759
## id (Intercept) 0.1186 0.3444
## Residual 0.6601 0.8125
## Number of obs: 494, groups: condition:id, 129; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.09015 0.08225 119.66236 25.411 < 2e-16 ***
## conditiondrug -0.24541 0.09890 64.12233 -2.481 0.0157 *
## time -0.22534 0.04734 373.25097 -4.760 2.78e-06 ***
## conditiondrug:time 0.09599 0.06602 370.24830 1.454 0.1468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.606
## time -0.019 0.015
## cndtndrg:tm 0.014 -0.018 -0.717
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 570.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8201 -0.5548 -0.2002 0.3057 4.1821
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.06786 0.2605
## id (Intercept) 0.14683 0.3832
## Residual 0.55775 0.7468
## Number of obs: 228, groups: condition:id, 58; id, 29
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.52463 0.11116 48.04003 13.716 < 2e-16 ***
## conditiondrug 0.12622 0.12038 27.94125 1.049 0.303
## time -0.29659 0.06221 168.51298 -4.768 4.01e-06 ***
## conditiondrug:time 0.08512 0.08824 168.78397 0.965 0.336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.545
## time -0.003 0.003
## cndtndrg:tm 0.002 -0.007 -0.705
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 481.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4627 -0.6814 -0.1119 0.5459 2.8380
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.02094 0.1447
## id (Intercept) 0.19729 0.4442
## Residual 0.76429 0.8742
## Number of obs: 174, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.23722 0.13681 34.31196 16.352 < 2e-16 ***
## conditiondrug -0.51833 0.13965 20.74077 -3.712 0.00131 **
## time -0.44598 0.08432 128.93920 -5.289 5.12e-07 ***
## conditiondrug:time 0.11365 0.11926 128.92380 0.953 0.34237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.510
## time -0.012 0.012
## cndtndrg:tm 0.008 -0.016 -0.707
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 391.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6610 -0.6255 -0.2142 0.4807 2.8207
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.04606 0.2146
## id (Intercept) 0.23621 0.4860
## Residual 0.40815 0.6389
## Number of obs: 173, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.75887 0.13240 30.35512 13.284 3.47e-14 ***
## conditiondrug -0.12052 0.11684 21.27910 -1.032 0.31389
## time -0.20351 0.06099 127.29143 -3.337 0.00111 **
## conditiondrug:time 0.06236 0.08679 127.67725 0.719 0.47372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.439
## time -0.003 0.004
## cndtndrg:tm 0.002 -0.008 -0.703Box RT
plot_combination('box_rt', 'CravingRT', limits_y = c(0.5, 3.5), imaging = FALSE)## [1] "###Condition z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA -0.789830 2.0756962 1.6805754
## neurocaps_mcr -0.789830 NA 2.4623103 2.1387617
## neurocaps_ocr 2.075696 2.462310 NA -0.6546852
## tacs 1.680575 2.138762 -0.6546852 NA
## [1] "###Condition by time z-scores###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 2.534853 1.091190 -0.1697659
## neurocaps_mcr 2.5348532 NA -1.416087 -2.4481921
## neurocaps_ocr 1.0911901 -1.416087 NA -1.1194922
## tacs -0.1697659 -2.448192 -1.119492 NA
## [1] "###Condition p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.42962703 0.03792206 0.09284541
## neurocaps_mcr 0.42962703 NA 0.01380452 0.03245497
## neurocaps_ocr 0.03792206 0.01380452 NA 0.51267043
## tacs 0.09284541 0.03245497 0.51267043 NA
## [1] "###Condition by time p-values###"
## tdcs neurocaps_mcr neurocaps_ocr tacs
## tdcs NA 0.01124945 0.2751893 0.86519428
## neurocaps_mcr 0.01124945 NA 0.1567501 0.01435751
## neurocaps_ocr 0.27518926 0.15675014 NA 0.26293022
## tacs 0.86519428 0.01435751 0.2629302 NA
## [1] "one"
## [1] "one"
## [1] "one"
## [1] "one"## Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## ICC1 ICC1_lower ICC1_upper ICC3 ICC3_lower ICC3_upper ICC3k ICC3k_lower ICC3k_upper var
## 1 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 -0.5 drug
## 2 0.671 0.36328604 0.8480396 0.671 0.35676460 0.8489036 0.803 0.5259049 0.9182778 -1.5 drug
## 3 0.195 -0.23004587 0.5609553 0.292 -0.13806489 0.6291989 0.452 -0.3203603 0.7724028 0.5 drug
## 4 0.045 -0.36852838 0.4476398 0.049 -0.37193485 0.4529254 0.093 -1.1843830 0.6234668 1.5 drug
## 5 0.498 0.11444423 0.7545390 0.498 0.10704348 0.7558644 0.665 0.1933862 0.8609599 drug
## 6 0.276 -0.14787226 0.6165991 0.276 -0.15446338 0.6189687 0.433 -0.3653618 0.7646457 -0.5 neutral
## 7 0.075 -0.34282183 0.4708619 0.075 -0.34941686 0.4732592 0.139 -1.0741651 0.6424657 -1.5 neutral
## 8 0.810 0.60106781 0.9159504 0.810 0.59626054 0.9164458 0.895 0.7470717 0.9564015 0.5 neutral
## 9 0.249 -0.17599277 0.5983798 0.306 -0.12247901 0.6386818 0.469 -0.2791478 0.7795068 1.5 neutral
## 10 0.365 -0.04945140 0.6745100 0.365 -0.05692284 0.6761878 0.535 -0.1207173 0.8068163 neutral
## 11 0.000 -0.40701472 0.4106858 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 -0.5DrugvNeutral
## 12 0.715 0.43418603 0.8701040 0.715 0.42808612 0.8708513 0.834 0.5995242 0.9309680 -1.5DrugvNeutral
## 13 -0.114 -0.49824159 0.3108622 0.000 -0.41324703 0.4132470 0.000 -1.4085895 0.5848192 0.5DrugvNeutral
## 14 0.325 -0.09457070 0.6490235 0.457 0.05410178 0.7320805 0.627 0.1026500 0.8453192 1.5DrugvNeutral
## 15 0.224 -0.20099234 0.5814810 0.227 -0.20541124 0.5854176 0.370 -0.5170253 0.7385027 DrugvNeutralEarly
## 16 0.467 0.07401810 0.7364226 0.467 0.06656248 0.7378312 0.637 0.1248168 0.8491403 DrugvNeutralLate
## 17 0.349 -0.06786693 0.6643120 0.349 -0.07532117 0.6660319 0.517 -0.1629131 0.7995428 DrugvNeutral## Saving 7 x 5 in image
## [1] "###tDCS MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 1086.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8130 -0.5455 -0.1869 0.2686 3.4076
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.1706 0.4130
## id (Intercept) 0.1763 0.4199
## Residual 0.6704 0.8188
## Number of obs: 396, groups: condition:id, 121; id, 65
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.38757 0.09252 99.37271 14.998 < 2e-16 ***
## conditiondrug 0.67315 0.11522 56.46129 5.842 2.68e-07 ***
## time 0.10055 0.04915 287.77919 2.045 0.0417 *
## conditiondrug:time -0.11214 0.07481 291.39762 -1.499 0.1350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.539
## time 0.078 -0.053
## cndtndrg:tm -0.051 0.058 -0.656
## [1] "###Neurocaps MCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 428.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8776 -0.6184 -0.1551 0.2547 3.1725
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.08263 0.2875
## id (Intercept) 0.14869 0.3856
## Residual 0.71649 0.8465
## Number of obs: 156, groups: condition:id, 48; id, 26
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.15054 0.12925 30.42153 8.902 5.65e-10 ***
## conditiondrug 0.83342 0.16704 20.31452 4.989 6.74e-05 ***
## time -0.05272 0.07641 102.21618 -0.690 0.491729
## conditiondrug:time -0.48453 0.12643 112.93184 -3.832 0.000209 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.498
## time 0.029 -0.018
## cndtndrg:tm -0.021 -0.043 -0.607
## [1] "###Neurocaps OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 346
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9132 -0.4724 -0.1693 0.3446 3.3533
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.1120 0.3347
## id (Intercept) 0.3614 0.6012
## Residual 0.4676 0.6838
## Number of obs: 139, groups: condition:id, 40; id, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.52428 0.17328 25.16252 8.797 3.77e-09 ***
## conditiondrug 0.26377 0.16006 15.67478 1.648 0.1193
## time 0.03842 0.07248 93.95191 0.530 0.5973
## conditiondrug:time -0.25231 0.10443 97.08704 -2.416 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.428
## time 0.001 0.000
## cndtndrg:tm 0.002 0.026 -0.693
## [1] "###tACS OCR###"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paste("value ~ condition * time + (1|id/condition)")
## Data: long_data
##
## REML criterion at convergence: 408.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6896 -0.5276 -0.1386 0.2052 4.5287
##
## Random effects:
## Groups Name Variance Std.Dev.
## condition:id (Intercept) 0.02208 0.1486
## id (Intercept) 0.42820 0.6544
## Residual 0.49879 0.7063
## Number of obs: 165, groups: condition:id, 44; id, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.27609 0.16272 25.57989 7.842 2.87e-08 ***
## conditiondrug 0.39445 0.11926 15.04231 3.307 0.00477 **
## time -0.04465 0.06858 111.57509 -0.651 0.51634
## conditiondrug:time -0.09104 0.09924 114.56134 -0.917 0.36086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtnd time
## conditindrg -0.360
## time 0.008 -0.007
## cndtndrg:tm -0.005 0.016 -0.689extract_slopes_beh <- function(measure, this_label, idps, prefix = 'dcr_'){
#same as above, but for behavioral data
#prefix will be dcr_tpre_ for tdcs/tacs and dcr_ for neurocaps
#measure will be box_rt, craving_rt, or craving
#measure <- 'craving'
#prefix <- 'dcr_tpre_'
#this_label <- 'Craving'
#idps <- idps_tdcs
#idps_tacs$dcr_tpre_response_craving_[0-7]
#idp_tdcs
#dcr_tpre_response_box_rt_0
#dcr_tpre_response_craving_rt_0
#idps_neurocaps_ocr$dcr_response_box_craving[0-7]
column_suffixes <- 0:7
these_cols <- c('id', paste0(prefix, 'response_', measure, '_', column_suffixes))
library(reshape2)
one_dataset <- idps[, these_cols]
long_data <- melt(one_dataset, id.vars = c('id'))
long_data$variable <- as.character(long_data$variable)
long_data$condition <- NA
long_data$number <- substr(long_data$variable, nchar(long_data$variable), nchar(long_data$variable))
long_data$condition[long_data$number %in% c('0', '2', '4', '6')] <- 'neutral'
long_data$condition[long_data$number %in% c('1', '3', '5', '7')] <- 'drug'
#put neutral in the intercept
long_data$condition <- factor(long_data$condition, levels = c('neutral', 'drug'))
#time = block number, just like for the imaging variables
long_data$time <- NA
long_data$time[long_data$number %in% c('0', '1')] <- 1
long_data$time[long_data$number %in% c('2', '3')] <- 2
long_data$time[long_data$number %in% c('4', '5')] <- 3
long_data$time[long_data$number %in% c('6', '7')] <- 4
#mean center on time
long_data$time <- long_data$time - mean(long_data$time)
library(lme4)
library(lmerTest)
library(ggplot2)
library(sjstats) #for icc of mixed effects models
all_results <- NULL
for (this_id in unique(long_data$id)){
#this_id <- 'AR316'
this_sub_data <- long_data[long_data$id == this_id,]
neutral_string <- paste0(this_label, '_slopeNeutral')
meth_string <- paste0(this_label, '_slopeMeth')
delta_string <- paste0(this_label, '_slopeMethMinusNeutral')
if (sum(!(is.na(this_sub_data$value[this_sub_data$condition == 'neutral']))) > 1 &
sum(!(is.na(this_sub_data$value[this_sub_data$condition == 'drug']))) > 1){
#subject made at least two ratings per condition
this_lm <- lm(paste('value ~ condition * time'), data = this_sub_data)
these_results <- data.frame(list(id = this_id, neutral_string = this_lm$coefficients['time'],
meth_string = this_lm$coefficients['time'] + this_lm$coefficients['conditiondrug:time'],
delta_string = this_lm$coefficients['conditiondrug:time']))
names(these_results) <- c('id', neutral_string, meth_string, delta_string)
} else{
#not enough ratings to estimate two slopes
these_results <- data.frame(list(id = this_id, neutral_string = NA,
meth_string = NA,
delta_string = NA))
names(these_results) <- c('id', neutral_string, meth_string, delta_string)
}
all_results <- rbind(all_results, these_results)
}
return(all_results)
}Corrplot including individual slopes and self-report
extract_one_slopes <- function( roi = '1', this_label = 'VMPFC', prefix = 'dcr_tpre_', idps = idps_tdcs){
column_prefixes <- c('stats_tdcsprelim_drug.r11.0.coef_mean_',
'stats_tdcsprelim_drug.r12.0.coef_mean_',
'stats_tdcsprelim_drug.r13.0.coef_mean_',
'stats_tdcsprelim_drug.r14.0.coef_mean_',
'stats_tdcsprelim_neutral.r11.0.coef_mean_',
'stats_tdcsprelim_neutral.r12.0.coef_mean_',
'stats_tdcsprelim_neutral.r13.0.coef_mean_',
'stats_tdcsprelim_neutral.r14.0.coef_mean_')
this_roi <- c('id', 'motion', paste0(prefix, column_prefixes, roi))
one_dataset <- idps[, this_roi]
long_data <- melt(one_dataset, id.vars = c('id', 'motion'))
long_data$condition <- NA
long_data$condition[grepl('neutral', long_data$variable)] <- 'neutral'
long_data$condition[grepl('drug', long_data$variable)] <- 'drug'
#put neutral in the intercept
long_data$condition <- factor(long_data$condition, levels = c('neutral', 'drug'))
long_data$time <- NA
long_data$time[grepl('r11', long_data$variable)] <- 1
long_data$time[grepl('r12', long_data$variable)] <- 2
long_data$time[grepl('r13', long_data$variable)] <- 3
long_data$time[grepl('r14', long_data$variable)] <- 4
#mean center on time
long_data$time <- long_data$time - mean(long_data$time)
#this_lme <- lmer(paste('value ~ condition * time + (1|id)'), data = long_data)
#for checking for NA's, looks like it's all good now
#print(long_data)
all_results <- NULL
for (this_id in unique(long_data$id)){
#this_id <- 'AR316'
this_lm <- lm(paste('value ~ condition * time + motion'), data = long_data[long_data$id == this_id,])
neutral_string <- paste0(this_label, '_slopeNeutral')
meth_string <- paste0(this_label, '_slopeMeth')
delta_string <- paste0(this_label, '_slopeMethMinusNeutral')
these_results <- data.frame(list(id = this_id, neutral_string = this_lm$coefficients['time'],
meth_string = this_lm$coefficients['time'] + this_lm$coefficients['conditiondrug:time'],
delta_string = this_lm$coefficients['conditiondrug:time']))
names(these_results) <- c('id', neutral_string, meth_string, delta_string)
all_results <- rbind(all_results, these_results)
}
return(all_results)
}
all_slopes <- extract_one_slopes('1', 'VMPFC', prefix = 'dcr_tpre_', idps = idps_tdcs)
all_slopes <- merge(all_slopes, extract_one_slopes('3', 'LSTG', prefix = 'dcr_tpre_', idps = idps_tdcs))
all_slopes <- merge(all_slopes, extract_one_slopes('4', 'RSTG', prefix = 'dcr_tpre_', idps = idps_tdcs))
all_slopes <- merge(all_slopes, extract_one_slopes('5', 'LVSTRI', prefix = 'dcr_tpre_', idps = idps_tdcs))
all_slopes <- merge(all_slopes, extract_one_slopes('6', 'RVSTRI', prefix = 'dcr_tpre_', idps = idps_tdcs))
all_slopes <- merge(all_slopes, extract_one_slopes('8', 'RAmy', prefix = 'dcr_tpre_', idps = idps_tdcs))
vas_data <- read.csv('../paper-dcr-temporaldynamics/data/MethVASData.csv')
vas_data <- vas_data[, c('record_id', 'redcap_event_name', 'mcs_vas', 'mcs_vas_2')]
names(vas_data) <- c('id', 'visit', 'craving', 'control')
library(reshape2)
vas_data_1 <- vas_data[vas_data$visit == 'before_pre_fmri_arm_1', c('id', 'craving', 'control')]
vas_data_2 <- vas_data[vas_data$visit == 'after_pre_fmri_arm_1', c('id', 'craving', 'control')]
vas_data_wide <- merge(vas_data_1, vas_data_2, by = 'id')
names(vas_data_wide) <- c('id', 'craving_pre', 'control_pre', 'craving_post', 'control_post')
vas_data_wide$craving_delta <- vas_data_wide$craving_post - vas_data_wide$craving_pre
vas_data_wide$control_delta <- vas_data_wide$control_post - vas_data_wide$control_pre
merged_data <- merge(all_slopes, vas_data_wide, all.x = TRUE)
last_use_data <- read.csv('../paper-dcr-temporaldynamics/data/ADUQ_2019-06-12_1407.csv')
merged_data <- merge(merged_data, last_use_data[, c('id', 'aduq_20a')], all.x = TRUE)
names(merged_data)[names(merged_data) == 'aduq_20a'] <- 'DaysSinceLastUse'
tableone_data <- read.csv('../paper-dcr-temporaldynamics/data/Table1Database66_ver2.csv')
merged_data <- merge(merged_data, tableone_data, all.x = TRUE)
idps_tdcs$methvsneutral_craving_selfreport_insidescanner <- (idps_tdcs$dcr_tpre_response_craving_1 + idps_tdcs$dcr_tpre_response_craving_3 +
idps_tdcs$dcr_tpre_response_craving_5 + idps_tdcs$dcr_tpre_response_craving_7 -
idps_tdcs$dcr_tpre_response_craving_0 - idps_tdcs$dcr_tpre_response_craving_2 - idps_tdcs$dcr_tpre_response_craving_4 - idps_tdcs$dcr_tpre_response_craving_6) / 4
merged_data <- merge(merged_data, extract_slopes_beh('craving', 'Craving', idps_tdcs, prefix = 'dcr_tpre_'), all.x = TRUE)
merged_data <- merge(merged_data, idps_tdcs[, c('id', 'methvsneutral_craving_selfreport_insidescanner')])
names(merged_data)## [1] "id" "VMPFC_slopeNeutral" "VMPFC_slopeMeth" "VMPFC_slopeMethMinusNeutral" "LSTG_slopeNeutral"
## [6] "LSTG_slopeMeth" "LSTG_slopeMethMinusNeutral" "RSTG_slopeNeutral" "RSTG_slopeMeth" "RSTG_slopeMethMinusNeutral"
## [11] "LVSTRI_slopeNeutral" "LVSTRI_slopeMeth" "LVSTRI_slopeMethMinusNeutral" "RVSTRI_slopeNeutral" "RVSTRI_slopeMeth"
## [16] "RVSTRI_slopeMethMinusNeutral" "RAmy_slopeNeutral" "RAmy_slopeMeth" "RAmy_slopeMethMinusNeutral" "craving_pre"
## [21] "control_pre" "craving_post" "control_post" "craving_delta" "control_delta"
## [26] "DaysSinceLastUse" "Age" "Education" "BMI" "Age.of.Meth.use.onset..years.old."
## [31] "Duration.of.Meth.use.at.least.once.a.week..years." "Cost.of.Meth..dollar.per.month." "Dose.of.Meth..gram.per.day." "History.of.Meth.injection.b..n....." "Days.of.Meth"
## [36] "Days.of.Alcohol" "Days.of.Alcohol.intoxication" "Days.of.Heroin" "Days.of.Methadone" "Days.of.Other.opioids"
## [41] "Days.of.Barbiturate" "Days.of.Sedative" "Days.of.Cocaine" "Days.of.Cannabis" "Days.of.Hallucinogens"
## [46] "Days.of.Inhalants" "Duration.of.current.abstinence..days." "Meth.Cue.Reactivity.Screening.score..0.100." "Meth.Withdrawal.Scale.score..0.24." "Motion..Rest.fMRI..Pre.tDCS"
## [51] "Motion..Task.fMRI..Pre.tDCS" "Motion..Rest.fMRI..Post.tDCS" "Motion..Task.fMRI..Post.tDCS" "Craving_slopeNeutral" "Craving_slopeMeth"
## [56] "Craving_slopeMethMinusNeutral" "methvsneutral_craving_selfreport_insidescanner"names(merged_data)[names(merged_data) == 'Meth.Cue.Reactivity.Screening.score..0.100.'] <- 'BaselineCueReactivity'
names(merged_data)[names(merged_data) == 'Duration.of.Meth.use.at.least.once.a.week..years.'] <- 'MethUseDuration'
names(merged_data)[names(merged_data) == 'Cost.of.Meth..dollar.per.month.'] <- 'MethCost'
names(merged_data)[names(merged_data) == 'craving_post'] <- 'Craving_post'
names(merged_data)[names(merged_data) == 'craving_pre'] <- 'Craving_pre'
names(merged_data)[names(merged_data) == 'craving_delta'] <- 'Craving_delta'
to_plot <- c('Age', 'MethUseDuration', 'MethCost',"DaysSinceLastUse",
'BaselineCueReactivity', "Craving_pre", "Craving_post", "Craving_delta",
"VMPFC_slopeNeutral", "VMPFC_slopeMeth", "VMPFC_slopeMethMinusNeutral",
"LSTG_slopeNeutral", "LSTG_slopeMeth","LSTG_slopeMethMinusNeutral",
"RSTG_slopeNeutral", "RSTG_slopeMeth", "RSTG_slopeMethMinusNeutral",
"LVSTRI_slopeNeutral", "LVSTRI_slopeMeth", "LVSTRI_slopeMethMinusNeutral",
"RVSTRI_slopeNeutral", "RVSTRI_slopeMeth", "RVSTRI_slopeMethMinusNeutral",
"RAmy_slopeNeutral", "RAmy_slopeMeth", "RAmy_slopeMethMinusNeutral")
to_plot <- c('Age', 'MethUseDuration', 'MethCost',"DaysSinceLastUse",
"Craving_pre", "Craving_post", "Craving_delta",
'methvsneutral_craving_selfreport_insidescanner',
'Craving_slopeMethMinusNeutral',
"VMPFC_slopeMethMinusNeutral",
"LSTG_slopeMethMinusNeutral",
"RSTG_slopeMethMinusNeutral",
"LVSTRI_slopeMethMinusNeutral",
"RVSTRI_slopeMethMinusNeutral",
"RAmy_slopeMethMinusNeutral")
library(corrplot)## corrplot 0.84 loadedthis_matrix <- cor(merged_data[, to_plot], use = 'pairwise.complete.obs')
pvals_raw <- cor.mtest(merged_data[, to_plot])
#get FDR corrected p-values to use for plotting
pvals_fdr <- pvals_raw$p
pvals_fdr[upper.tri(pvals_fdr)] <- p.adjust(pvals_raw$p[upper.tri(pvals_raw$p)])
pvals_fdr[lower.tri(pvals_fdr)] <- p.adjust(pvals_raw$p[lower.tri(pvals_raw$p)])
col2 <- colorRampPalette(rev(c("#67001F", "#B2182B", "#D6604D", "#F4A582",
"#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE",
"#4393C3", "#2166AC", "#053061")))
png('corrplot_slopes_sxs.png', width = 1000, height = 1000)
corrplot.mixed(this_matrix, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1,#, lower.col = "black",
upper.col = col2(50), lower.col = col2(50), p.mat = pvals_fdr, sig.level = 0.05)
dev.off()## png
## 2